Design and Implementation of a Localization System Based ... · ABSTRACT TITLE: Design and Implementation of a Localization System Based on a RF Multipurpose Platform AUTHORS: LUZ

Design and Implementationof a Localization System

Based on a RFMultipurpose Platform

by

Luz Karine Sandoval Granados

Thesis submitted as partial requirement for the degreeof

MASTER OF SCIENCE IN ELECTRONIC

ENGINEERING

from

Instituto Nacional de Astrofısica, Optica y

ElectronicaOctober 2011

Tonantzintla, Puebla

Supervised by:

Ph.D P. Jorge Escamilla Ambrosio, Ms.c JorgePedraza Chavez, INAOE

c©INAOE 2011The author gives permission to the INAOE to

reproduce and distribute copies in whole or in parts ofthis thesis.

.

“ To my beloved parents

To my dear sister Laura ”

Luz K.

Acknowledgment

I would like to express my gratitude to people who had helped and inspired me during my master

study.

To my Teachers, my advisor Dr. Jorge Escamilla and also the members of instrumentation de-

partment, they instruct me and advice me during this two years, I am sure that their knowledge and

comments influence this work in a positive way.

To my friends and colleges on Electronic’s Department at INAOE, special thanks to Hector,

Jhoan, Fabian and Jaqueline who have been a real support during my stay in this country. Also I am

grateful to Erika, Moises, Abel, Jesus, Dulce, they thought me a quite part of the Mexican culture and

also made of those two years a joyful and enrichment experience.

To my family, for their support and encoragement throught my entire life and in particular, I am

grateful to my syster Laura, whose assistance was an important asset to this thesis.

To the Consejo Nacional de Ciencia y Tecnologia (CONACYT-Mexico) for providing me with

financial support throughout my master.

ABSTRACT

TITLE: Design and Implementation of a Localization System Based on a RF Multipurpose Platform

AUTHORS: LUZ KARINE SANDOVAL

KEY WORDS: RSSI, Wireless location system, Location awareness, Neural Networks, Multilayer

Perceptron.

DESCRIPTION: In this work the development of a radio frequency (RF) indoor location systems

using Texas Instruments device ez430-RF2500 is proposed, this kind of systems are commonly used

to perform many monitoring and tracking tasks. They pose a real challenge because for indoor en-

vironments the RF model of propagation is much more variable than that for outdoors environment:

signal levels vary greatly depending on specific features such as the layout of the building, the con-

struction materials and the positions of the sensors.

The development proposed in the paper was designed to work with basic tools, practical consi-

derations such as the small size, cost and power constrains were taken into account. A fixed set of

low-cost, low-power transceivers together with information commonly available as Received Signal

Strength Indicator (RSSI) and Link Quality Indicator (LQI) were used to convey a proper response.

The algorithm proposes is based on a neural network approach. A Multilayer Perceptron is trained

off-line to learn the relationship between the local position of a mobile node and the common features

of a packet received (RSSI and LQI) from four base stations (anchors).

The tests showed promising results, twenty one fixed points were evaluated in a closed room (3.9m

x 7m), four base stations were transmitting continuously in different channels and a mobile node took

in time intervals the RSSI and LQI from packets received from each anchors.

iv

Contents

1 Introduction 1

1.1 Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.2 Drawbacks of Electromagnetic Wave Propagation . . . . . . . . . . . . . . . 3

1.2 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.2.1 Indoor and Outdoor Environments . . . . . . . . . . . . . . . . . . . . . . . 7

1.2.2 Radio Frequency Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.2.3 Overview of the Different Approaches . . . . . . . . . . . . . . . . . . . . . 8

1.3 Research Objectives and Organization of this Document . . . . . . . . . . . . . . . 10

1.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.3.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.3.3 Organization of this Document . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Structure of a Positioning System 13

2.1 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.1 Texas Instrument ez430-RF2500 development tool . . . . . . . . . . . . . . 14

2.1.2 Libelium Waspmote Devices . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Operational Phase of a Positioning System . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.1 Measure Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.2 Estimation Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3 Elements of the Proposed System 27

3.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1.1 Testing Time of Transmission and Reception Packets . . . . . . . . . . . . . 28

3.2 Operational Phase - Measurement Parameters . . . . . . . . . . . . . . . . . . . . . 30

3.2.1 TDOA Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.2.2 Interferometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.2.3 Radio Signal Strength Approach . . . . . . . . . . . . . . . . . . . . . . . . 31

vi CONTENTS

3.3 Infrastructure Establishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.1 Elements of the System Infrastructure . . . . . . . . . . . . . . . . . . . . . 35

4 Experimental Results 434.1 Positioning System based on The TI ez430-RF2500 Transceivers . . . . . . . . . . . 44

4.2 Experimental Testbed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.3 Radio Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.3.1 Source of statistical dispersion . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.3.2 Evaluating the Propagation Model Approach . . . . . . . . . . . . . . . . . 49

4.4 Fingerprinting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.4.1 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5 Conclusions and Future Work 615.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Bibliography 63

List of Figures

1.1 Example of a Location Awareness System . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Relationship between distance T-R and coverage area [13] . . . . . . . . . . . . . . 4

1.3 Field Regions for transmissions using a typical antennas . . . . . . . . . . . . . . . 5

1.4 Mechanisms of propagation waves . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.5 Synoptic Diagram Section 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.6 Synoptic Diagram Section 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1 Development Kit Texas Instrument ez430-RF2500 . . . . . . . . . . . . . . . . . . 14

2.2 Simple MSP430-CC2500 interconnection diagram [22] . . . . . . . . . . . . . . . . 15

2.3 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4 Communications Protocol [24] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5 Waspmote Development Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.6 Waspmote API header . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.7 Frontal-face of a location system, Angle of Arrival Technique . . . . . . . . . . . . 20

2.8 PinPoint Calibration Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.9 Interferometry Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.10 AOA Interferometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.11 Trilateration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1 Measuring Transmission Time on Waspmote Devices . . . . . . . . . . . . . . . . . 28

3.2 Connection Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.3 Details of Current Consumption on ez430-RF2500 . . . . . . . . . . . . . . . . . . 30

3.4 Antenna’s Radiation Pattern of TI ez430-RF2500 [41] . . . . . . . . . . . . . . . . . 34

3.5 Rutine for each Base Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.6 Algorithm used in the mobile device . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.7 State Machine of Mobile Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.8 Identifier of a new point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.9 State Machine for control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

viii LIST OF FIGURES

3.10 Presentation of printed data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.11 Algorithm used in the gateway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.1 Experimental SetUp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.2 RSSI as a function of distance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.3 RSSI as function of distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.4 Measuring the RSSI at fixed point . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.5 Filtering Process: (a) Signal in time domain; (b) Effect of impulse train; (c) Frequency

Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.6 Experimental Testbed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.7 Effect of antenna’s anisotropy on RSSI measures - Test 1 . . . . . . . . . . . . . . . 52




4.11 Attenuation Pattern in a Crowded Place . . . . . . . . . . . . . . . . . . . . . . . . 56

4.12 Neural Network Estimations on Different Points . . . . . . . . . . . . . . . . . . . . 60

4.13 Neural Network Estimations on Different Points . . . . . . . . . . . . . . . . . . . . 60

List of Tables

1.1 Relevant RF Positioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1 Test results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.1 Results of The Neural Network Training . . . . . . . . . . . . . . . . . . . . . . . . 58

4.2 Results of Neural Network Validation . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.3 Results of Neural Network Validation . . . . . . . . . . . . . . . . . . . . . . . . . 59

Chapter 1

Introduction

Wireless communications as a foundation of a localization system, has been a widely researched

technology that offers a reliable and low cost system. Its popularization began with the invention of

mobile phones, devices that in few years have gotten to root in people, being today an indispensable

item for most of the population [1], it is in continuous improvement at an extent that new features and

technologies are constantly added.

The widespread success of wireless communication has led to the development of a quite signif-

icant number of systems and standards that enable transmissions between different kind of data and

devices. Today it is possible to transmit audio, text and video at high rates and at reasonable levels of

security and reliability, popular systems like Bluetooth and Wireless Local Area Networks (WLAN)

are the result of this intense research, they made easier the communication between devices in such

way that there are an explosive growth of wireless devices like printers, headphones, microphones,

speakers, keyboards, which all have now their wireless version [2].

Because of these new devices have become simultaneously less expensive and more powerful,

many paradigm shifts have taken place, wide variety of sensors allow the measurement of different

conditions including: temperature, pressure, humidity, speed, motion, distance, light and proximity.

Therefore, the development of more practical and dynamic systems is now possible and then the

research has been orientated to personalized environments.

Personalized environments require devices that can passively or actively determine their location,

the location awareness (term used to refer to this kind of devices) is a highly targeted market opportu-

nity. This is because the location awareness gives meaning and context to measures taken, it introduces

a new field of application in many areas: wireless security, monitoring and tracking systems are the

most popular. This technology is needed in any system that delivers information to users based on

their physical location.

Nowadays, there are multiples ways to identify where is a mobile target. Basically, these systems

use some type of wave (infrared, radio frequency, ultrasonic, etc.) and relate its features (amplitude,

2 Introduction

frequency or phase) to distance. Various technologies are available: GPS (Global Positioning System),

cellphone infrastructure and wireless access points, but neither of them have been labeled as ”the best”.

There are many constrains and restrictions in this problem: power consumption; range and accuracy of

sensors devices; protocols and methodologies needed to support the distributed signal processing and

information storage [3, 4], are some examples. Most of all these systems have been designed without

taking into account the environment, which represents the biggest one restriction, wave’s diffraction,

reflection and scattering is highly affected by it.

The favorable features described in the last paragraphs, motivated the research work presented

in this thesis, which addresses the development of a location system that, considering different con-

strains, make it feasible for many applications. This development was designed to work in indoor

environments with basic tools, practical considerations such as small size and low cost were taken

into account. A methodology based on hardware constrains and the different approaches proposed in

literature were used to define each element of this system. A review of the features of this system are

presented in the following paragraphs.

A fixed set of low-cost, low-power transceivers as the Texas instruments ez430-RF2500 and also

information commonly available as Received Signal Strength Indicator (RSSI) and Link Quality In-

dicator (LQI) were used to convey a proper response. The algorithm proposed is based on an off-line

training technique as the neural network approach, the Multilayer Perceptron approach is employed

to establish the relationship between the local position of the mobile node and common features of a

packet received (RSSI and LQI). Once the system is trained, on-line location can be performed.

1.1 Basic Concepts

1.1.1 Definition

A location awareness system (figure 1.1) is a wireless sensor network that uses a number of observable

parameters (angle, velocity, signal strength) to determine the position of a target. This collaborative

process has three basic components:

Base Stations: These elements are generally transmitters of radio, infrared or ultrasound signals,

they act as passive components emitting continuously this type of wave. Satellites, GSM towers or

Wi-Fi access points are some examples of them.

Terminal devices: They play a crucial role in positioning systems, as the system’s target, they

act sometimes as transmitters and receivers depending on infrastructure and protocols. These devices

usually have a small size, mobility and low power consumption; mobile phones, Wi-Fi enabled tags,

laptops, PDAs and hand-held GPS receivers are the most popular.

Control Unit: It manages the information and carries out the signal processing, depending on

which system is implemented, it could be the terminal device itself (decentralized processing) or an

1.1 Basic Concepts 3

Base Station

Data Transmittions

Bidirectional Communications

Base Station

Terminal

Device

Control Unit

Base Station

Base Station

Figure 1.1: Example of a Location Awareness System

independent device, which performs the processing of all the information (centralized system).

1.1.2 Drawbacks of Electromagnetic Wave Propagation

Most positioning systems make use of wave propagation theory to determine the target position, some

of them use time difference of arrival to do this approximation [5, 6], others use the signal strength

[7–9] or a combination of them [10, 11], however, the propagation itself have many drawbacks, as is

explained in the following sections:

Free Space Path Loss

When receivers and transmitters have a clear, unobstructed line-of-sight (free space propagation) the

power of wireless transmission decrease with the square of the distance [2,12], this statement is based

on the fact that electromagnetic waves propagates in all directions covering the surface area of a sphere

(Fig 1.2), then as a wave propagate out from the source its radiant energy remains the same, but the

energy per unit area (energy density) decreases.

In communication systems, the free space propagation model takes into account others parameters

like antenna’s features and intrinsic system losses. This relationship is described by the Friis free

space equation,

Pr(d) =PtGtGrλ

2

(4π)2d2L(1.1)

4 Introduction

Receiver

S1

S2

S3

Transmitter

Figure 1.2: Relationship between distance T-R and coverage area [13]

where Pr(d) and Pt denote the received and transmitted power, Gt and Gr are the antenna gain in

transmitters and receivers, λ is the wavelength, d is the transmitter-receiver separation distance and L

is the system loss due to transmission line attenuation, filter losses in the communication system.

Equation 1.1 shows how sensitive is a communication system. The signal attenuation (path loss)

is an important parameter in a proper design, it is defined as the difference between the effective

transmitted power and the received power [2] and is measured in dB. The intrinsic path loss is given

by:

PL(dB) = 10logPtPr

= −10log[GtGrλ2

(4π)2d2] (1.2)

Free Space Model Constrains

Propagation of electromagnetic (EM) waves imply the interaction of electric and magnetic fields

through a medium (metals, water, air, etc.). If EM waves change its propagation medium (for ex-

ample when these waves leave the guiding influence of wires and move to space), then this cause

disturbances in the EM fields. This behavior can be categorized as a function of distance, where three

boundary regions are defined (Fig 1.3), these regions are: the ”Near-Field”, ”Transition Zone”, and

”Far-Field” [12].

1.1 Basic Concepts 5

Transition Far FieldNear Field

Zone

Figure 1.3: Field Regions for transmissions using a typical antennas

When the distance between source and receiver is too small (less than 2λ approximately), the

energy from the source is not radiated in a radial direction, then other effects have to be taken into

account in order to do a proper model [12]. This is because the relationship between the electric field

(E) and the magnetic field (H) becomes very complex; this regions are known as Near-Field when

distance is less than λ and Transition Zone when the distance is between λ and 2λ [12, 13]. The Far-

Field or Fraunhofer region is the operation region where most propagation models are proposed (the

Friis free space model only makes a proper prediction in this region [2]). The distance traveled by EM

waves in this region is enough (farther than two wavelengths) to guaranty stable electric and magnetic

fields (The E and H fields are mutually perpendicular to that direction and to each other) and, because

of the linear relationship between its fields, many approximations can be done [2, 12].

Multi-propagation Signal Effect

Wireless communication systems have one fundamental limitation, their transmission path has many

uncertainties; it can vary from simple line-of-sigh to one that is severely obstructed by different objects

(buildings, foliage or people). The transmission is affected by three basic propagation mechanisms

(Fig 1.4):

* Reflection: Occurs when a wave changes the transmission medium, in this case the reflection

takes place when the wave impinges an object big enough that its dimensions overcome the

wavelength of the propagating wave [2]. As a consequence, part of the wave bounces off and

changes its current direction.

In electromagnetic waves, the reflection coefficient is highly influenced by the obstacle’s con-

ductivity; when the plane wave imping a perfect dielectric, part of its energy is reflected and the

remainder is transmitted, but when the obstacle is a perfect conductor, all the incident energy

6 Introduction

Reflection

source

(a) Reflection [14]

Scattering

source

(b) Scattering [14]

Diffraction

Aperturethrough

source

Shadow

Zone aroundObstacle

Diffraction

(c) Diffraction

Figure 1.4: Mechanisms of propagation waves

is reflected back into the original medium [2]. It makes the estimation of this phenomena a

hard issue, daily objects are made of more sophisticate materials, and the reflection coefficient

(Fresnel reflection coefficient) also is function of the angle of incidence and the frequency of

the propagation wave.

The wave reflection phenomenon is one of the biggest problems in wireless communications

systems, it causes a phase reversal, so when a message is transmitted, the receiver will see a

combined signal (one component is the line-of-sight signal and the others are produced by re-

flections). This effect could be very harmful for the system, with proper conditions ( path length

of the reflected signal and wavelength) a interference constructive or destructive is possible [12].

* Diffraction: It is a phenomenon present when waves pass through an opening or around sharp

irregularities, it produces secondary waves that allow to surround obstacles or pass through

openings. The amount of diffraction depends on the wavelength and the sharpness of edges, if

the wavelength is smaller than the obstacles there isn’t any effect [2].

Huygen’s principle can explain why diffraction is produced, it states that any point of the wave-

front is a source of a secondary wave. Thus, diffraction is useful because permits wireless

communications between points where the transmission path is obstructed by objects opaque to

radio waves (mountains, buildings, etc.) [2, 12].

* Scattering: It is a mechanism of propagation induced by the roughness in different obstacles,

when a wave imping this kind of surface the signal diffuse in all directions adding more spurious

1.2 State of the Art 7

signals to the spectrum [2, 12].

The amount of scattering is influenced by the degree of roughness, wavelength (λ) and the angle

of incidence (θi). The maximum height (hc) of this protuberances is given by the Rayleigh

criterion 1.3. Many common objects such as lamp posts and trees tend to scatter energy so, this

phenomenon is present in all wireless communications [2].

hc =λ

8sinθi(1.3)

As it has been seen, the environment in a wireless communication system has many signals trav-

eling over multiple paths; reflection, diffraction and scattering are unavoidable. Thus, the receivers

have to be designed to resolve variations of signal strength, frequency distortion, time delay spread

and fading [12].

1.2 State of the Art

1.2.1 Indoor and Outdoor Environments

Real time localization systems can be classified depending on the environment where they are used, in-

door and outdoor applications are very popular, however, they suppose different challenges. Outdoors

systems require an expensive infrastructure, their mobile devices need an excellent power management

and a wide coverage area. Besides, their base stations have to be designed to endure different envi-

ronmental conditions (humidity, strong winds, rain, etc). On the other hand, indoor systems require

more signal processing, their model of propagation is much more variable than outdoor environments,

specific features such as the layout of the building, the construction materials and the positions of the

sensors produce multiple signal propagation [2].

Wide coverage areas have already an almost reliable system, the GPS has been integrated to many

devices such as smartphones, automobiles, personal computers, airplanes, to name a few. GPS is a

worldwide system based on 24 satellites, located around 30000 km above the Earth’s surface, they

orbit at 11000 nautical miles and send their current coordinates at GPS receivers, which use this

information to estimate their position by trilateration. This technique needs at least three satellites

with line of sigh to get a reasonable accuracy response (less than 10m) [15].

In closed areas, many systems have been proposed, which vary in different aspects like the infras-

tructure, protocols and technology used (it can be Infra-Red (IR), ultrasonic, bluetooth, ultrawide-band

or Radio Frequency (RF) [1-4]). Analyzing the wide range of possibilities it is found that IR based

systems have an accuracy of 0.07 meters at the expense of cost in terms of hardware, power consump-

tion and coverage area. In the same way, ultra-wideband systems have also an small estimation error

(around 0.1 meters) but its performance is restricted by the Federal Communication Commission due

8 Introduction

to the hight power transmissions [15]. Moreover, many others systems have been proposed which

illustrate the fact that accomplishment of an excellent performance is not a warranty of a successful

solution.

1.2.2 Radio Frequency Approach

In real applications different conditions have to be taken into account in order to stablish the real

feasibility of the system, this conditions are not only limited by the environment, the infrastructure

itself could compromise the performance, therefore, an adequate integration of hardware and software

in addition to robust communication protocols are needed; one possible short cut to this problem is

the use of a mature technology, like the use of radio frequency devices.

The RF transceivers have all kind of support due to its continuous research, and more important,

its successful implementation on printed boards, which offers many advantages like portability, low

costs and low power consumption in addition to the inherent benefits of using RF waves [7]. In table

1.1 the different implemented proposals illustrating the adaptation of this technology are presented.

1.2.3 Overview of the Different Approaches

Table 1.1 is a summary of the most relevant RF positioning systems proposed up to date, as it is seen,

the development started in 2000 with RADAR, a location technology that get the estimations using

a two fold approach; one of them is based on a propagation model enhanced with some empirical

variables and the other one uses a fingerprinting of the environment. The maximum error obtained in

RADAR is about 4.3 meters. Nevertheless, this error could be considered as acceptable if the coverage

range is taken into account. In this particular system, the error change at 200 meters with line of sight,

at 50 meters in a semi-open place and at 25 meters in a crowed place. Despite the quite big error

obtained, this was a promising result because few constrains were taken into account.

The analysis of different sources of noise and distortion were not included in RADAR, but this

is a real challenge in current systems. Subsequents technologies considered different kinds of inter-

ferences, for example, COMPASS and EKAHAU use probabilistic algorithms to deal with human

obstructions and movements, the accuracy achieved is now around 1 to 3 meters, but still depends a

lot on the environmental conditions. The next step was to incorporate prediction algorithms, it was

done by HABITS which claims to be able to enhance any positioning system by incorporate the patron

behavior of people. In the EKAHAU system, HABITS increases the precision of the system, allowing

accuracy estimations in blind spots.

Other lines of investigation aimed to get good results by changing the infrastructure. Systems

like SPOT-ON are autonomous devices that perform all the different tasks of a positioning system,

however, the localization can be only obtained as a relative distance to other users, thus, the accuracy

1.2 State of the Art 9

depends on the size on the group.

Appreciable differences (in hardware and software) on proposed systems can be observed when

the measuring distance technique is changed. For example, the use of the time of arrival technique

need in most RF based approaches the use of additional hardware. The interferometric approach also

needs another changes but this time at a software level (special protocols and settings have to be set)

in order to stablish the proper conditions to produce the desired signal, however, the accuracy is better

(around 0.12 meters) but the requirements are high. As a consequence, before choosing the general

features of this work, it is imperative to understand each one of the different approaches.

On figures 1.6 and 1.5 a summarize with the main find outs discussed in this chapter are presented.

{

{{

{* Diffraction

* Reflection

* Scattering{ parameters like signal strength, time of flight or phases differences.

{Physical Phenomena Involved:

Environment:* Outdoors

* Indoors Still open research problemGPS become the standard

Technology Employed: Wireless RF devices

Objetive: Determine the local position of a transceiver by the use of signals observable

* Mechanism of waves propagation

* Attenuation * Friis EquationWireless Location System

Figure 1.5: Synoptic Diagram Section 1.1

{{

{

{{

Wireless RF Indoor

Location System

* 802.11 Infrastructure{

{* Own Infrastructure

Infrastructure: {

Operational Mode:

* Estimation Phase

* RSS

* Propagation Model

* TOA

* AOA{* Measure Phase

{ Statistical Methods.

* FingerprintingStatistical Methods. + Lateration

Neural Networks

Statistical Methods.

Lateration* AOA or TDOA Algorithm

* Pinpoint * Lanzisera * Radio Interf AOA

* Spot−ON * RFID * Telosb

* RADAR * Trapeze * COMPASS

* In−building * EKAHAU * HABITS

Lateration

Figure 1.6: Synoptic Diagram Section 1.2

10 Introduction

1.3 Research Objectives and Organization of this Document

1.3.1 Problem Definition

A positioning system for indoor environments is still an open research problem that requires the con-

sideration of multiple factors, the main constrains is the environment where this system will be im-

plemented, any object with sharp edges or rough protuberances could be a source of multipath or

attenuation, inherent properties as the conductivity of the material where the wave impinges is also an

important parameter that determine the percentage of the energy transmitted or reflected. Therefore,

diffraction, reflexion and scattering are unavoidable effects on signal’s propagation when the condition

of line of sight cannot be guaranty,

These undesired effects make the task of modeling the wave propagation a hard issue, where

random variables have to be incorporated. To overcome this constrain, many proposals have been

done, all kind of variables have been used like RF, IR, or acoustic signals but neither of them can

guaranty a hight accuracy at reasonable costs.

The best results were obtained when additional hardware was incorporates, making indeed, a

highly accurate system. But at extend that it cannot be considered a feasible option, its costs and fea-

tures could restrict its massive implementation. Then, the objective of this work is design a positioning

system employing a commercial off-the-shelf RF development tool as start point.

As can be seen from above discussion, the design of a positioning system implies a careful selec-

tion of each one of the basic elements of this application, that can be cope with the inherent uncertain-

ness produced by the wave propagation in any wireless system.

1.3.2 Objectives

General Objective

The objective of this research is to design an indoor positioning system based on a commercial off-

the-shelf multipurpose RF development tool that using only the information provided by the com-

munication system of these devices will be able to determine the local position of a target in a real

environments like an office or a closed room.

SSpecific Objectives

* Evaluate the performance of two multipurpose RF development tools as Libelium Waspmote

and Texas Instrument ez430-RF2500 in order to determine the feasible use of them in a posi-

tioning system.

* Analyze the different approaches proposes in the literature in order to determine which of these

techniques are suitable for the hardware provided.

1.3 Research Objectives and Organization of this Document 11

* Create the necessary protocols to ensure a proper operation of the positioning system.

* Make experimental tests in order to discard and select a suitable technique.

* Carry out experiments on controlled and crowded environments to test whether the different

proposals can be used with the provided platform.

1.3.3 Organization of this Document

The remainder of this thesis is organized as follows, Chapter 2, presents a description of the different

elements of a RF localization system, which will be useful to stablish the different techniques that

can be implemented. In chapter 3 some tests and further analysis over the hardware allowed to define

the features of this system. In chapter 4 some experiments are carried out in order to evaluate the

performance of the proposed approach and finally the conclusions and future work are discussed in

chapter 5.

12 Introduction

Ref.Technology

NameAccuracy Dist. Measuring

TechniquesLoc. Estimation

Techniques Infrastructure Year

[7] RADAR 3 - 4.3m RSS & SNR -Database -Base stations (BS) 2000-Prop. model -WLAN devices

[16] Spot-ON Depends on RSS -Empirical Model -Relative location 2000cluster size -Triangulation to other devices

[15] Trapeze 10 - 25ma RSSI -Fingerprinting -Server on 802.11 2000LA-200 Network + Wifi tags

[15] RFID 0.25 - 4.19m AOA Range & Angle -Reader + Antennas 2005Radar measurement -Passive Tags

[17] Lim, Kung 3m RSS Euclidean distance -802.11 Infrastruct 2005Hou, Luo client - nodes - Wifi devices

[5] PinPoint 1.27 - 4.27m TOA RF Lateration -Base stations 2006-Tag Hardware

[18] COMPASS 1.65m RSS -Fingerprinting -WLAN infrastruct. 2006-Prob. Algorithm b -Digital compass

[8] In-building 1.18 - 2.16m RSS Neural Networks - 3 Modems 3COM 2006- 1 Laptop

[6] Lanzisera, 1 - 3m TOA Hardware App. - 2 Sensor Motes 2006Lin, Pister

[9] Adaptive Dist. 0.5m c RSSI -Statistical methods - 7 Base Stations 2007Estimation -Artificial Neural N. - 1 Mobile Device

[15] EKAHAU 1 - 3m RSSI -Fingerprinting -802.11 Network 2008-Prob. Algorithm d -Tags + Wifi clients

[19] Paschalidis, 2.26m RSSI Pdf Interpolation 30 Motes doing e 2009Li, Guo Prob. Descriptors a dual function

[20] Telosb 2.23m RSSI Neural Network - 3 Active BS 2010Neural App. LQI - 1 Mobile mote

[11] RF Received 0.05 - 0.12m Radio Inter- Phase differences - 2 transmitter nodes 2010Phase ferometric f Algorithm - 1 Receiver

[10] Radio Interf. 3 degrees AOA Radio Interf. - Array of 3 motes 2010AOA measurement - 1 target

[21] HABITS g Predictability Software Bayesian Filter EKAHAU 201180% Tool System

Table 1.1: Relevant RF Positioning SystemaThis low accuracy is compensated by its wide coverage (around 4000 devices at once)bReduce effect of human bodycUnder specific conditionsdReduce Error of human movements and wall effectsePlaced on differents landmarks to construct the pdf familiesfOnly works using Berkeley Mica2 Motes have been reportedgPredict de next target position by studing the human movements habits

Chapter 2

Structure of a Positioning System

In the last chapter location awareness systems were presented, their features and constrains reveled

that it is not possible to reach a general solution to this problem, it had to be delimited in order to

attain reasonable results. In principle, a discussion about the work environment and technology used

in different proposals were done. The indoors approach and RF technology was chosen considering

that this kind of system aims to resolve an open research problem employing a well supported and low

cost system as RF transceivers. Once the system was defined, a review of the different alternatives

in this field was done, this study allowed to identify the different features of a Location Awareness

System, which have to be taken into account in order to make a proper design.

In this chapter the basic structure of a positioning system is presented, as well as the different

strategies employed on each of these elements, which will be the base for a proper system selection.

2.1 Infrastructure

This approach is intended to operate under a wireless development platform, this platform offers

excellent resources, its design promotes the development of complete projects without many concerns

about different protocols and subsystem interfaces. They got it, combining the performance of a micro-

controller with other commonly available input/output systems, as is the case of ADCs, pushbuttons,

RF transceivers, etc.

The system is basically composed by gateways and boards, a gateway constitutes a bridge be-

tween incompatible hardware like laptops and transceivers, which otherwise could not be able to

communicate. On the other hand, a wireless board has an autonomous system that allows monitoring

tasks of almost any kind of variables, due to the different input/output ports and the multichannel RF

transceiver integrated.

Taken into account these features, two development platforms were considered:

14 Structure of a Positioning System

(a) Debugging Device and Target Board.

(b) Battery Case.

Figure 2.1: Development Kit Texas Instrument ez430-RF2500

2.1.1 Texas Instrument ez430-RF2500 development tool

The ez430-RF2500 development tool was specially designed for remote monitoring applications, it

has an entire set of tools which enable the implementation of complete projects without much effort.

Figure 2.1 shows the components of the basic kit:

* Battery Board: It is basically a case where two AAA batteries are connected in order to keep

the necessary current powering the target board, enabling portability of this system.

* USB debugging device: It acts as the gateway of the system, it is responsible for communications

between laptops and target boards. It has multiple functions in order to properly debug appli-

cations using IAR Embedded Workbench Integrated Development Environment (IDE) or Code

Composer Essentials (CCE).These tools allow also to analyze programs at low level (changes

of different registers instructions by instructions), reaching more efficiency when program opti-

mization is necessary.

* Target Board: The target board constitutes the heart of this device, it has the whole communi-

cation system (RF transceiver, antenna, crystal, etc), the different input/output ports and also a

microcontroller that manages and saves the instructions codified. The target board is removable

2.1 Infrastructure 15

(see figure 2.5(a)) so once a reliable code is realized, the next step is to compile and debug it in

order to save it on the target’s microcontroller, unfortunately it has a limited memory, thus the

efficiency of programs is the main concern.

Programming and Compilation

Assembler or C++ can be employed as coding languages. For this reason, the communication between

the MSP430 and other input/output systems like the CC2500 are done through registers, this level of

instructions is too low so in order to facilitate the coding task, different libraries were created. Thanks

to that, the port configurations (UART settings) were not necessary done by the final user, only basic

settings have to be specified like the data rate of transmition to the USB port. Other useful libraries

simplify the change of the different radio settings.

Communications with the RF Transceiver CC2500Communications are enabled by the RF transceiver CC2500, which is a flexible system that allows

different settings, for example, it is possible to change the bandwidth and frequency of the channels,

the power of transmissions and the kind of modulation that will be used. All of this features can be

configured depending on the MSP430 instructions. In figure 2.2 a diagram of the different intercon-

nections between the MSP430 and the CC2500 are shown, four of this six wires form the serial link

(SPI) responsible for enabling digital communication.

MSP430 CC2500

Clock

SPI Serial Interface

4 wires

Digital Communication

System Interruption

User Interruption

Output Ports conected to interruptions

F2274

Figure 2.2: Simple MSP430-CC2500 interconnection diagram [22]

As a consequence, the whole communication is enabled by a set of instructions that have to be

defined each time a wireless application is carried out. However, a software tool was developed to

make easy the task of changing the different settings that the communication system has. The SMART


RF STUDIO has a wide variety of transceivers integrated to the Texas instrument development tools to

choose, it allows the configuration in two modes, this tool allows to establish different RF parameters

an also to set data rates of packets to be transmitted.

Transmissions and Reception

The transmissions and receptions are limited by the size of the packet (the maximum size is 64B),

however, there are a wide variety of transmission modes supported, burst and continuous transmissions

are available, but this only increases the number of packets to transmit [23]. The CC2500 provides an

useful support for packet handling. In the figure 2.3 the diagram of the packet format is shown. In

this architecture, additional information like sync word, preamble and length field must be included

for the demodulation process, but it is possible to send additional information like Cyclic Redundancy

Check (CRC) and the address byte.

Address Field

8xn bits

System Fields

packet.frame (User provided fields)

Data Field

destin

ation

payload

8b

its

Len

gh

t

8xn bits 16

bit

s

source16/32bits C

RCSync WordPREAMBLE

Figure 2.3: Packet Structure

Communications ProtocolThe target board is a very flexible hardware that can be manage by registers, this feature enables

the development of partial or complete protocols depending on the specifications required. By default

the SimpliciTI protocol is used, it is a low-power RF protocol elaborated by Texas Instruments that

enables RF transmissions and receptions in a simple way. Its architecture is shown in figure 2.4, three

basic layers can be identified:

* Application Layer: in this layer the specific protocols and methods to support peer to peer

communications are established, most of them are not intended to be part of the customer de-

velopment environment, its functions are more related to security and reliability of wireless

connections [24, 25].

* Network Layer: The Network Layer has only management tasks, it is responsible for the routing

of packets transmitted and received [24].


DATA−LINK

* Ping: debugging purposes.

* Link: Support link management.

* Joint: Guard entry to network.

* Security: control encryptions keys.

* Mgmt: general management.

Management Layer

NWK

DRIVERS: C Lybraries MRFI − BSP

APLICATION

LAYER

NETWORK

PHY

Figure 2.4: Communications Protocol [24]

* Data Link/PHY: this layer is composed by two entities, the Board Support Package (BSP) and

the Minimal RF Interface (MRFI), both of them provide the necessary functions to allow the im-

plementation of a common API for all supported RF and Board devices. Basically it constitutes

a set of libraries that make easy the programing of target boards [24, 25].

2.1.2 Libelium Waspmote Devices

The Libelium Waspmote hardware technology (figure 2.5) is a development tool designed and manu-

factured to be used by a diverse audience (engineers, system integrators and also consultancy compa-

nies are part of the end users), for this reason it offers a complete set of additional modules that can be

easily integrate to the target, in order to expand the functions of this devices without many concerns

about the interfaces needed to warranty a proper operation. In Figure 2.5(c), the available sockets are

shown. It also offers different input/output options, like and UART connector, a USB port and also

the traditional analog and digital ports.

Thinking on this project, the following tools are necessary:

* XBee 802.15 Module: this device allows wireless communications under the standard IEEE

802.15.4, which defines the physical level and the link level, operating at 2.4GHz in 16 channels

with a bandwidth of 5 MHz. The power of transmissions is adjustable on five levels (-10 dBm

to 0 dBm).

* Battery: Waspmote uses a rechargeable lithium-ion battery with 3.7V nominal voltage.

* Waspmote Gateway: It is the interface between PC and XBee modules, this device is used to


(a) Waspmote Board (b) Waspmote Gateway

(c) Board Frontal Face (d) XBee Module

Figure 2.5: Waspmote Development Tool

receive data from remote motes as well as to modify or to consult the XBee’s configuration

parameters.

* Waspmote Boards: This device is the main part of this kit, it is responsible for monitoring

tasks in remote places. It manages different elements as battery, Xbee modules and sensors

board (accelerometers, temperature or presion sensor) using a microcontroller ATmega 1281

and expandable memory (SD card).

Programming and Compilation

In order to create and loads projects, Libelium developed a set of useful tools:

Aplication Programming Interface (API): it was created to handle different functionalities on


Waspmote devices, such as interruptions or transmission channels, this API has been developed in

C/C++ and it is divided in classes and data structures (a total description can be found as part of wasp-

mote support [26]). This instructions facilitate the programming process because it includes of all

modules integrated in Waspmote, as well as the automation of routine tasks.

Waspmote-IDE compiler: this software was developed by Libelium as part of the Waspmote sup-

port, it is an useful tool designed to operate under Linux, Windows or Mac-OS, It makes possible

create, compile projects and also allows to see the data present on the serial port.

Transmission and Reception

Transmission and reception functions are realized by an independent module integrated to the Wasp-

mote Board. This element can be chosen by the user, depending on the project’s requirements, there

are a wide variety of mudules, they differ in some aspects like technology, communication protocol or

power transmission. However, this work was realized with the XBee 802.15.4 modules.

The whole management is done by Waspmote Libraries. In this case the Waspmote XBee files

(WaspXBeeCore.h; WaspXbeeCore.cpp, WaspXBee802.h and WaspXBee802.cpp) are required. These

libraries contain the necessary functions to set different features of the system like the protocol, model

and frequency used, as well as the packet parameters. The packet is the fundamental unit created to

transmit information, it is structured in API libraries using the packet XBee structure, in the figure 2.6

a diagram of the packet structure is presented, in this description it is possible to observe the complex-

ity of the protocol, it is distributed in layers where different fields are defined in order to warranty a

reliable and secure transmission.

Destination

Start

AP

I ID

DATADelimiter

numberfragment Type

IDSource

IDData

IDID Options

RF Data

Using the function SendXBee

is possible send data

at low level.ID

Check

SumLENGHT

Figure 2.6: Waspmote API header


2.2 Operational Phase of a Positioning System

The procedure to obtain the actual position of a target is carried out by any positioning system in

two stages, in the first stage recollection of interest data is realized; in the next stage integration and

analysis of this data allow the estimation of the targets position.

2.2.1 Measure Phase

There are many variables that can be considered as possible options to determine the distance between

two transceivers in RF Location awareness systems. Examining the different proposals (see details in

table 1.1), there are five basic approaches:

Angle of Arrival AOAThis technique makes use of the anisotropic phenomenon in reception patterns of directional anten-

nas (beam-forming) to estimate the angle of incidence at which signals arrive at the receiving sensor.

The target position is then determined combining data about at least two reference points, to illustrate

its principle of operation in figure 2.7 a representation of a location system with three elements is

presented. In this scheme the position of base stations (A and B) is known as well as its incidence

angles, then the target position (xc and yc) can be determined using the cosine rule.

The main concerns in this approach is the uncertainness nature of signal strength, which not only

vary as a consequence of the use of anisotropic antennas, but it is also affected by variation of signal

amplitude due mainly to features of transmission path and interference with other signals transmitted.

RF positioning systems based on AOA techniques are supported, in most cases, by an specialized

hardware that deal with the uncertainness of measurements by improving the antenna’s direction-

(Xb,Yb)

B

A

C

Base

Station ABase

Station B

Target(Xc,Yc)

(Xa,Ya)

Figure 2.7: Frontal-face of a location system, Angle of Arrival Technique

2.2 Operational Phase of a Positioning System 21

ality [27, 28] or by enhancing the infrastructure of the whole system [29]. However, there are other

approaches based on radio interferometry [10,30] that obtain good results without many modifications

on radio system (they will be discussed as part of the selection process in the next chapter).

Radio Signal Strengththe radio signal strength is a common parameter present in most radio transceivers, since every

communication system has an Automatic Gain Control (AGC), which being part of the control loop

is responsible for keeping the signal level in the demodulator invariable to sudden changes product of

diffraction, reflection and scattering phenomena.

In order to accomplish this task, the AGC calculates the mean of the input signal (applying a low-

pass filter over the input), and then, this result is compared with a predefined target level to set the new

gain setting. Therefore, it is an indirect measure of the signal strength [31].

Since the radio signal strength is an standard measurement, a wide variety of approaches using

this techniques have been developed. The general approach consists in recording and processing the

signal strength of transmissions from multiples base stations, positioned in strategic places.

Examining relevant works (see table 1.1) it is possible to identify two categories, systems relying

on the use of a propagation model and those supported by empirical measurements.

* Radio Propagation Method: Using Friis free space equation 1.1 it is possible to predict the

way in which the propagation of electromagnetic waves are attenuated. However, this model

constitutes a naive simplification of the problem, since the propagation of waves is affected by

reflection, diffraction and scattering.

Even thought the effects of propagation suffer from uncertainness mainly due to environmental

conditions, an other model of propagation based on empirical evidence is accepted as a good

estimator, it makes use of random variables to cope with these undesired effects [7,32]. That is,

Pr(d)[dBm] = Po(do)[dBm]− 10 ∗ np ∗ log[d

(do)2d2] +Xσ (2.1)

where Pr(d) and Po denote the received and reference power, d is the interest distance, docorrespond to the reference distance, np is an empirical parameter that measure the rate at which

the RSS is attenuated and Xσ represents the random effect of shadowing and correspond to a

Gaussian distributed random variable with zero mean.

* Fingerprinting Method: This technique aims to construct an RSS profile of the coverage area.

Based on the nature of electromagnetic propagation, which is highly influenced of the envi-

ronment, unique patterns are identified by recording information about the radio signals as a

function of the user’s location.


This was one of the first techniques used, however, the constrains imposed by the off-line pro-

cessing which involves a large memory space as well as a fast computation processing, delayed

its use until nowadays where it has been used in combination with different probabilistic and

estimation algorithms [33, 34].

Times and Difference of Time Techniques

This techniques basically consists on measuring the time of flight of a signal using hardware or

software solutions. In RF positioning systems the use of this technique is limited, the big constrain is

the time of response of the devices, which has to be in the order of microseconds because the waves

travel at the speed of light.

Different approaches have been proposed, for example, in 2008 an application under the ZigBee

protocol was proposed, it uses temperature-compensated crystal oscillators [35] in order to get a proper

response. However, few systems offer a feasible solution, this is the case of Pinpoint [5] which using a

sophisticated protocol removes synchronization problems of conventional oscillators (see figure 2.8).

In figure 2.8 the Pinpoint protocol is presented, it basically consists on keeping track of the time at

which transmission and reception take place, therefore it operates under devices with a medium access

control (MAC) clock stamping (conventional laptops) getting a reasonable accuracy (over 3 meters).

Time

ta1 ta2 ta4

tb1 tb2 tb3 tb4

Base

Station A

Base delay

delay

Station B

delayta3

Time

Figure 2.8: PinPoint Calibration Phase


Interference

Base

Station B

Unmodulated Carrier f1 Unmodulated Carrier f2

Station A

Base

Base

Station C

RSS measured

RSS measured

Mobile

Device

Figure 2.9: Interferometry Technique

Radio Interferometric

This technique constitutes the most recent way to measure the distance employing radio transceivers.

This system was proposed six years ago in Vanderbilt University, it uses the Mica2 Berkeley motes to

build a radio interference system. This devices thought, are simple and cheap, can be tunned at very

low frequencies [36, 37]. The basic idea is to produce a signal that can be easily measured with ordi-

nary devices, therefore it is necessary to reduce the hight frequencies at which RF systems operate. To

accomplish it, two unmodulated waves are generate and continuously transmitted by two base stations

at very close frequencies (in the order of hertz). Once the waves collide, the composite signal will

have a low frequency envelope easily detected by commonly available hardware (figure 2.9).

This technique has been used in two different system, one of them tries to estimate the local

position of a sensor by comparing the phase and frequency of the interference signal measured in two

different transceivers (figure 2.9) [11, 36, 37]. Another approach determines the angle of arrival of a

signal combining the directionality of the antenna and the measure of a very differentiable signal, as

it is the case of the interference wave generated due to the hight peaks of power emmitted, in figure

2.10 a basic scheme of the mobile device is shown.


Interference Generated

3 XSM Motes

Figure 2.10: AOA Interferometer

2.2.2 Estimation Phase

Once data is recorded, it needs to be processed in order to eliminate discrepancies produced by the

different sources of noise; and also to associate this information to a common framework. In position-

ing systems there are many algorithms which integrate the information coming from multiple inputs

giving an estimate position, they can be classified as:

TrilaterationTrilateration is an analytical location method that uses distance measurements to identify the posi-

tion of an object. As is shown in figure 2.11(a), the problem consists to find the intersection between

the range of three devices (in this case base stations) that in the ideal case represent a unique point

therefore only one position, but in real environments the noise is unavoidable then the response is an

area of possible location 2.11(b).

In order to attain a unique response, a common approach is to take multiple measures of this point

and then calculate its mean value.

AC

B

(a) Ideal Case

B

A C

(b) Error due to Noise Environ-ment

Figure 2.11: Trilateration


Deterministic algorithmsThese algorithms attempt to minimize an statistical distance, this mean they try to reduce the dis-

tance between two statistical objects. Depending on the kind of object used it receives a different

name, some of them are:

* Euclidean Metric: tries to minimize point to point samples, this is the common approach of

minimizing the error between calibration values and samples.

* Manhattan Distance: defines the minimum distance between two points as the sum of the abso-

lute differences of their coordinates.

* Mahalanobis distance: calculates the distance between two random vectors in the same distri-

bution, it takes into account the correlations of the data set as another important factor to define

if a point belongs or not to a set.

Probabilistic algorithmsThese algorithms consider a degree of randomness as part of their logic, in general they try to model

the uncertainness of a model as a probabilistic problem where the likelihood of a particular location is

defined in the calibration phase which is considered as an a priori conditional probability distribution.

These algorithms have been used in some popular positioning system as is the case of EKAHAU

which use Bayesian probability inferences to achieve a proper response.

Sophisticated algorithmsAnother approach to find the actual position of a target is to use of another technique that tries to

model this problem as a pattern recognition problem based on non-linear discriminant functions, this

is the case of neural networks, a relative new approach that could eliminate discrepancies through a

learning process.


Chapter 3

Elements of the Proposed System

Any positioning system can be defined by its infrastructure and its operation mode, it refers to the

way in which this systems determines local distances (between two motes) and estimates the global

position of a target (by combining the information of different motes). At was illustrated in the last

chapter, the evaluation of these features was an important step in order to delimit the problem, which

by using general purpose hardware achieve feasible solutions.

In this chapter the selection of the different elements of the proposed system will be discussed,

firstly, a feasibility analysis of the available hardware is presented, this important issue is a critical

factor on the designing phase. Secondly, different operation modes will be evaluated and tested based

on the hardware constrains. Finally, the complete system will be presented including each of the

different proposed communication protocols needed to warranty a proper operation.

3.1 Hardware

Based on the features described in the last chapter, this technology has different benefits and also

constrains. On the one hand, Waspmote devices have many support systems which can be easily

integrate to the system, this modular platform promotes practical deployments where each device is

specially designed by its particular purpose, but this functionality has the counterfeit effect of system’s

latency and also the restrictions imposed by the API which control the access to configuration settings.

On the other hand, Texas Instruments ez430-RF2500 devices offer simpler system where the user has

a complete access to the platform, these devices can be programmed at low level using Assembly or

C language. But the integration with additional software requires of new protocols (entirely designed

by the user), also basics knowledge about the configuration and operation of this hardware is needed.

These features make difficult the selection of one of these devices, further analysis will be done in

order to make sure which of them is the best election for this particular application.

28 Elements of the Proposed System

Signal Generator Gateway PC

dataWaspmoteDevice

(a) Experiment’s Functional Diagram

PW

3

T

t

V

(b) Waveform Generated

transmition time

V

t(c) Signal Recovered

Figure 3.1: Measuring Transmission Time on Waspmote Devices

3.1.1 Testing Time of Transmission and Reception Packets

One critical factor in a real time positioning systems is the latency associated to the estimation, time is

the biggest constrain, so in order to select which platform could be useful, the delays on transmission

and reception of packets was measured.

Waspmote Devices:

In order to measure the transmission time, a simple test was designed. Using one Waspmote board and

gateway a remote sensing of an analog signal was built (figure 3.1(a)), the board basically was respon-

sible of monitoring one of the analog input ports which was connected to a signal generator producing

a rectangular pulse as is shown in figure 3.1(b). Once the process begins, each data is continuously

transmitted, thus the analog wave is now uniformly sampled at the frequency of transmission, as is

illustrated in figure 3.1(c).

The goal of this test was to evaluate the sampling rate of this system, so inputs at different fre-

3.1 Hardware 29

quencies were tested, the number of samples recovered by the gateway in each particular case and also

an estimation of the sampling frequency are shown in table 3.1.

FrequencyHz

SamplesRecovered

SamplingFrequency

65 68.8351.059 66 69.894

65 68.83533 68.739

2.083 32 68.73932 68.739

Table 3.1: Test results

ez430-RF2500 Development Tool

Tests done on this platform basically consisted on analyzing changes on current consumption in order

to establish when a mote is executing some function, in this case transmission and reception of packets

was studied.

Current consumption can be visualized by connecting a resistor between the board and batteries

as is illustrated in figure 3.2. Two test were done, one of them aims to measure the latency produced

by pure packet transmissions and the other one, studied delays on the complete demodulation process.

R

Target Board

Battery

Figure 3.2: Connection Diagram

In this test two boards were used; one of them was programed to detect when a packet is received

(enabling the function MRFI-RxCompleteISR()) and the other one was continuously transmitting the

same packet.

Using the battery board, the current consumption in those devices was measured, this process is

detailed in figure 4.1, where is observed how the time employed to transmit or receive packets is in

the same order (about 2.32ms), however the power consumption is different, due to the nature itself of

this functions, being opposite processes, where modulation and demodulation of signals took place.


(a) Transmission (b) Reception

Figure 3.3: Details of Current Consumption on ez430-RF2500

Technology Employed

Time is the most important concern to the development of a positioning system.The reliability and

usefulness of this system depends on how this variable is controlled. Therefore, taken into a count

that the Texas Instruments ez430 devices have hardware solutions instead of software solutions to do

the management of packets and information (SPI serial communication), makes this platform faster

and more reliable than the devices which although being a modular architecture, execute these func-

tions through other devices as is the case of Waspmotes, Moreover, Waspmotes offer more tools and

memory (it can be expanded to 2GB), the use of additional hardware add more latency in this system,

as was illustrated on the test carried out, where the additional protocols make the process of sending

packets too slow, it takes around 15ms to send one data byte, in comparison with the ez430-RF2500

modules which only need 2.32ms, which in this case compromise the whole system performance.

Considering that the purpose of this work is to build a localization system using an off-the-shelf

sensor platform, the ez430-RF2500 development tool offers better features than the Waspmote devices.

Because its highly integrability implies the use of few devices to accomplish each task, it simplifies

the whole operation of the system doing the communication protocols more straight and also reducing

the latency of this system which constitute a big concern for any real time positioning system.

3.2 Operational Phase - Measurement Parameters

The evaluation of positioning systems carried out in chapter 2, presented an overview of the different

ways to use a radio frequency system as a sensor of distance. There are three basic approaches (time,

signal strength or interferometry), each of them have an important number of purposes with different

3.2 Operational Phase - Measurement Parameters 31

levels of accuracy. However, in most cases they have to be discarded because, they are not a feasible

solution. In other cases further analysis have to be done in order to select the most useful tool.

3.2.1 TDOA Systems

In order to use the TDOA approach it is necessary has a system with advanced clocks or a software to

compensate the side effect of inaccurate clocks. Examining different approaches using this technique,

Pinpoint is a solution that seems to fit this system specifications, it uses common hardware (inaccurate

clocks) to accomplish about three meters of error. However, this system needs devices with a medium

access control clock stamping in order to get a response of microseconds in time-stamping, this critical

feature is not present in platforms as TI ez430-RF2500 and Waspmote.

3.2.2 Interferometry

The primary requirements to use this approach is the hardware ability to generate unmodulated waves

at really close frequencies. Evaluating the hardware available (TI ez430-RF2500 platforms), unmod-

ulated waves can be generated with the CC2500 radio transceiver by configuring the mote to enabling

OOK modulation and send an infinite packet of random values; but the transmitting frequency (carrier

frequency) cannot be adjusted at level of hertz, the crystal used to generates the carrier frequency is af-

fected by many issues such as capacitive loading errors, ageing and temperature. Hence these systems

have an inherent drift (Xppm), which can be calculated by the next expression [38]

Error =fcarrier ∗Xppm

106(3.1)

Considering the transmitting range of these devices (2400MHz - 2485MHz), and the minimum

crystal error (10ppm), the error oscillates between 24kHz-24.85kHz. These range of values are too

hight to produce the desired interference in comparison to other works [36, 37] which get a proper

response by creating unmodulated waves with only a few hertz of difference (0.1Hz - 5Hz).

3.2.3 Radio Signal Strength Approach

The measurement of packet’s parameters by the communication system is also another option widely

used in the literature. As was discussed in the last chapter, the radio signal strength is widely used

in different applications, it is common to many communication systems and even it is part of popular

protocols as the IEEE 802.12.4, which includes this information on the received packet. In figure

2.6, there are some features of the demodulation process accessible to the users (through STATUS-

Registers) that could be useful.

Two elements of the structure RX-Metrics, are commonly used, they are the signal strength and

the link quality indicator (RSSI and LQI, respectively). Although these localization systems are based


on the signals strength, the link quality added more credibility to the measurea taken, this is because

it is an indirect way to measure how pure is the received signal.

RSSI: The RSSI is the name as this parameter is known, it stands Received Signal Strength Indicator

and it is basically a measurement of the RF power received by the motes. In the Texas Instruments

ez430-RF2500 devices, the RSSI is a digital value which can be read continuously from the RSSI

status register. This value is continuously updating until a message is detected to be indexed to the

packet received (this parameter is part of the RX-metrics).

LQI: The link quality indicator (LQI) is another important parameter common to many transceivers,

it is also a digital value which can be reader from the LQI status register, basically it is a measure of

the quality of demodulation, it is obtained by comparing the constellation symbols with the expected

(for minimum shift keying (MSK) demodulation it is a measured of the error in the carrier frequency).

Therefore, if the input signal is strongly affected by noise, its LQI is low and zero when the input is

only noise.

Sources of Error

InterferenceThe explosive growth of wireless technology leads to big changes in the portion of radio spectrum

where industrial, scientific and medical devices work, this band known as 2.4GHz-ISM band has

been overcrowded with a large number of devices (especially laptops which boots Wi-Fi technology)

that vary not only in form but also in operation, each of them needs an specific support, hence, this

band has an spread number of protocols such as IEEE 802.11, IEEE 802.15.4, ZigBee and Bluetooth

coexisting together. Thus, interference in these kind of applications is an unavoidable issue, the figure

3.4(a) shows the seriousness of this matter, relating the signal strength with position and power of

each foreign device.

The overlapping of the different standards in the ISM band forces to do a complete study of

the radio signal strength in which the TI ez430-RF2500 works (between 2.4GHz-2.485GHz) [39].

However, this analysis only takes into account the WLANs and Wi-Fi spectrum, due mainly to its

power of transmission, which is enough to compromise the system proper operation.

An evaluation of this feature was done using InSSiDer 2.0, an open-source Wi-Fi scanning soft-

ware that inspects networks tracking the strength of the received signal of each point detected, pro-

viding not only the number of devices connected but also the channel occupancy. Different scenarios

were measured and the multiple existing networks forced an examination of alternative frequency

bands. Studying this spectrum, it was observed that Channel 14’s occupancy rate is almost null and

this seems to be an standard, which can be justified if it is considered that Wi-Fi is regulated by each

3.2 Operational Phase - Measurement Parameters 33

country and only a few countries enable the use of this channel, then most wireless devices are not

designed to select it. As a consequence the system proposed in this thesis will be operating in this

band of frequencies (between 2,473GHz - 2484GHz) [40].

Antennas IssueThe TI ez430-RF2500 uses a surface mountable antenna that copes with the requirements of this

platform, hence, small size, low cost and good performance are part of its features. It is basically a

metal bar (Silver-Nickel-Tin alloy) printed on a rectangular ceramic base (9,5mm x 2,0mm) [41].

The main concern for this application is the radiation pattern of its antenna, which can be used to

design the arrangement of transceivers, hence the antennas can be disposed to transmit and receive in

the direction of its maximum transference of energy. The radiation pattern of the TI ez430-RF2500

is defined on the antenna’s datasheet [41] and it is presented in figures 3.4(a), 3.4(b) and 3.4(c). This

antenna displays hight directionality over the yz plane, which is an indirect source of distorsion and

errors on RSS measurements.

Analysing the description of the antennas gain, it seems necessary to take measures to counteract

this undesired effect, a more isotropic radiation patter is necessary to warranty reliable measurement.

In this work this issue leads to the use of two transceivers instead of one to measure the RSSI and

LQI. Therefore, an arrangement back-to-back of two devices form the mobile unit of this system

(localization target).


(a) xy Plane

(b) xz Plane

(c) yz Plane

Figure 3.4: Antenna’s Radiation Pattern of TI ez430-RF2500 [41]

3.3 Infrastructure Establishment 35

3.3 Infrastructure Establishment

Once an strategy has been selected as the best option considering the specifications of the system, now

it is necessary to define how the whole system is going to operate, that is, what will be the sensors

arrangement and communication protocols in order to accomplish a proper tracking.

In this context, there are basically two approaches that can be taken, one of them aims to use a

centralized unit which is responsible for the whole information processing. In this system, a set of

transceivers are responsible of the generation of RF signals and the rest of them measure the param-

eters of interest and send this information to the control unit in order to process and calculate the

position.

Other valid approach is to use of a non-centralized system where the mobile device and the control

unit are the same, this ad-hoc system is orientated to achieve a better performance by reducing the

latency added when data is sent to a foreign unit, but its accuracy is limited by the number of associated

neighbor systems.

Since this work uses a general purpose technology, the TI ez430-RF2500, an ad-hoc system is

out of consideration. The available hardware is a highly integrated system, then it only has the ba-

sic requirements. The main control unit has a limited Flash and RAM memory (32KB and 1KB)

which is not enough to cope with the specifications of the localization system, which needs not only

a wide number of data, but also it uses an algorithm to reduce the inherent noise produced by the

environmental conditions and the wave propagation characteristics.

3.3.1 Elements of the System Infrastructure

The infrastructure of the positioning system proposed in this tesis can be divided in three basic ele-

ments:

Base Stations

The base stations are an array of ez430-RF2500 sensors disposed in such a way to maximize the area

of coverage area, these fixed points are responsible for the generation of the measured signals, they

basically are programed to be continuously sending a message at 431.03 Hz, by an specific channel,

which was chosen taken into account the source of interference, over the range of channel 14 on the

WiFi protocol (2473MHz - 2495Hz).

Four base stations are used at different frequencies (2475 MHz, 2478 MHz, 2481 MHz and 2484

MHz), it implies the use of multiple functions, an interruption has to be set and also the MRFI-transmit

function which will be set once the push button is activated (the interruption), the algorithm is shown

in figure 3.5.


Enable pushbutton P1REN as interrumption.

Init ports using BSP function. Configure port P2 as output

mrfiSpiWriteReg(CHANNR,255); MRFI_WakeUp(); MRFI_RxOn(); __bis_SR_register(GIE+LPM4_bits);}void MRFI_RxCompleteISR(){ //P1OUT ^= 0x02;

#pragma

void Port_1 (void){ //P1IFG &= ~0x04;

mrfiPacket_t packet; packet.frame[0]=8+20; //P1OUT ^= 0x01; for (counter=1;counter<5;counter++){ packet.frame[9]=170; packet.frame[10]=170; MRFI_Transmit(&packet, MRFI_TX_TYPE_FORCED);

}

uint16_t counter;

vector=PORT1_VECTOR__interrupt

t.

int main(void){

MRFI_Init();

;

BSP_Init();

P1REN |= ; P1IE |=

}

"mrfi.h"#include "radios/family1/mrfi_spi.h"#include

; P2DIR |= 0x08

0x04

mrfiSpiWriteReg(PATABLE, 0xFF );

0x04

* Initialization for CC2500 radio chip.* Set the maximum transmission power (1 [dBm]).* Set the transmission channel for each base station.* Enable the interruption service.

}

the structure packet.frame and two bytes with the number 170 (10101010) are continuously sending .

becaus the interrumption flag is not initialized (P1,IFG)

Once setting pushbutton, this function is enabled, different features of packet are specified using

This rutine only finish when the device is off, it is

Figure 3.5: Rutine for each Base Station

Mobile Device

This unit is integrated by two sensors, they are disposed to operate in cooperation with the control unit,

basically they measure parameters of the packet and send it to the control unit for further processing.

The mobile device is responsible for the recollection of data, in this case it periodically scans the

four channels, defined previously, in order to measure the RSSI and LQI of the signals generated by

the different base stations. Basically, they read a determined number of data bytes from an specific

base station, then they sent this information to the control unit and repeat this procedure until each

base station is considered.

In figures 3.6 this algorithm is detailed, figure 3.6(a) show the different tasks done by mobile

device before it is enabled to measure RSSI and LQI values (main() function) and figure 3.6(b) show

the process to measure and send the information to the control unit.


0x04;

}

MRFI_WakeUp(); __bis_SR_register(GIE); while(1);}

void MRFI_RxCompleteISR(){}

int main(void){ int8_t j=0,i; BSP_Init();

mrfiPacket_t packetToSend;"radios/family1/mrfi_spi.h"#include "mrfi.h"#include

P1REN |= MRFI_Init();

P1IE |= 0x04;

MRFI_RxIdle(); mrfiSpiWriteReg(CHANNR,190); while(j<3){ TXString("Ch 190\n",7); for(i=9;i<49;i+=1){ packetToSend.frame[i]=0; } packetToSend.frame[0]=50; packetToSend.frame[49]=’N’; packetToSend.frame[50]=’X’; MRFI_Transmit(&packetToSend, MRFI_TX_TYPE_FORCED); j++;

message is sent . This flag is used to mark the

registers are configured, the radio chip is set in transmission mode (channel 190), and then a

In this stage the radio chip CC2500 is waiting foran interrumption.

using bsp and mrfi lybrariesInitialization of different elements

beggining of mearuments in an specific point.

Once the device is turn on and the different

#pragma vector=PORT1_VECTOR__interrupt void Port_1 (void)

Change the transmission channel and repeat the whole process.

while(k<200){ packetToSend.frame[0]=50; for(i=9;i<49;i+=1){ if(i%2){ dato=buffer[k]; } else{ dato=bufflqi[k]; k++;} packetToSend.frame[i] = dato; } packetToSend.frame[49] = ’B’; packetToSend.frame[50] = ’0’+ (a%10); MRFI_Transmit(&packetToSend, MRFI_TX_TYPE_FORCED); }

//P1IFG &= ~0x04; uint8_t cout=0; int8_t rssi,lqi; int8_t buffer[200],bufflqi[200];

while(cout<220) { if(cout==0){ MRFI_RxIdle(); mrfiSpiWriteReg(CHANNR,210); ch=210;a=1; }

MRFI_RxIdle(); mrfiSpiWriteReg(CHANNR,225); ch=225;a=2;} if(cout==110){ MRFI_RxIdle();

ch=240;a=3;} if(cout==165){ MRFI_RxIdle(); mrfiSpiWriteReg(CHANNR,255); ch=255;a=4; } MRFI_RxIdle(); MRFI_RxOn();

rssi=MRFI_Rssi(); lqi=mrfiSpiReadReg(LQI); if (lqi){

MRFI_RxIdle();

Set the transmission channel, this channel is continuously changing depending of the number of samples taken for a determined base station.

The rutine is repeated two hundred times

while(i<200){

buffer[i]=rssi; bufflqi[i]=lqi; i++;}}

uint8_t i=0,ch,a,dato,i,k=0;

if(cout==55){

mrfiSpiWriteReg(CHANNR,240);

mrfiSpiWriteReg(CHANNR,200);

k=0; MRFI_RxIdle(); mrfiSpiWriteReg(CHANNR,ch); cout ++; channel=0; }}

Save the RSSI and LQI values

detected, then this values are saved on a two arrays (buffer and bufflqi) to be sent later.

on variables rssi and lqi. If a message is

Send data to the control unit, because of the limited size of packet, is neccesary send multiples times until the whole information si sent.

Figure 3.6: Algorithm used in the mobile device


The operation of these elements can be described by the state machine (figure 3.7), in this algo-

rithm six states can be identified:

* Configuration: In this state the whole system is configured, then the drivers are set (loading of

MRFI and BSP libraries) and input/output ports are specified.

* Identifier Point: Taken into account that the whole information will be saved by the control in a

text file, it is necessary to specify when there is a change of position, this is done by sending a

packet with a clear identification as can be seen on figure 3.8.

* Idle: Once the push button is activated, an interruption is enabled and then the target board

is ready to receive data. This state is used to change settings and start again the process of

reception of data.

* Setting Channel: this task is a periodic process, which consists in setting the necessary states to

change the channel, basically a variable is counting times in which a measure takes place and

base on this the transition to a different channel happen, beginning with the lowest one.

* Saving Data: In this state the target reads the RSSI and LQI registers and saves these values in

variables which acting as buffers recollect this information to be sent on a posterior process.

* Sending Data: Once the buffers are filled (that is when two hundred data are stored) the infor-

mation is ready to be sent and a process of packaging begins. Recollecting 40 bytes of measures

each time, this information is continuously transmitted to the control unit.

of 40B

Identifier

Sending

Data

Setting

Channel

Data

Saving

PointConfiguration

Waiting for200 data

Push button=’1’

Idle

LoopPeriodic

Periodic Loop

Periodic Loop

Periodic Loop

Sending packets

Figure 3.7: State Machine of Mobile Unit


[N X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ]

Figure 3.8: Identifier of a new point

Control Unit

This element is integrated by the gateway and a laptop which is responsible for the signal processing,

in order to give a proper estimation. Therefore, two basic tasks will be done, one is the recollection of

data (it is executed by the gateway) and the other one is the information processing (laptop’s work).

Considering that the procedure to estimate the actual position of a target depends on the estimation

technique choosen, in this section only the recollection stage will be discussed. The algorithm is

presented in figure 3.11.

The recollection stage can be described by the state machine illustrated on figure 3.9, there are

four basic states which will be explained in the following paragraphs.

* Configuration: In this state different tasks of configuration are carried out, first at all the loading

of the MRFI and BSP libraries and also the settings of the UART interface (communication

with the serial port) are defined to enable a proper communication between the target board and

laptop.

* Packet Reception: When a message arrives an interruption is activated, then it is possible to

control how many packets will be received on an specific channel. In this state the target is

prepared to print the information or to change the reception channel.

* Set Channel: In this state three channels are periodically set, each of them are enabled by an

specific operation. Considering that there are two devices continuously sending packets by its

Setting

Channel

Idle

PrintingData

Push button=’1’

Num received = 184

Configuration

Figure 3.9: State Machine for control unit


specific channel, these measures are recollected on a sequential way, beginning by the same

sensor each time and then switching to other one, once this operation finishes, they pass to a

third state where it waits for the identification packet.

* Print Packet: In this state the information is sent to the serial port where it is read and saved

for its posterior analysis. The information is disposed in such a way that is possible to specify

which base station generates this signal and also the transceiver used (A or B). Then, different

flags were printed in addition to the information measured (RSSI and LQI). In figure 3.10 an

example of this arrangement is shown.

[rssi lqi rssi lqi rssi ...............| A | a ]


Channel where the measureswas taken.

40 bytes of data


Flag indicating the sensor used



Figure 3.10: Presentation of printed data


P2DIR |= 0x08; P1REN |= 0x04; P1IE |= 0x04;

This function is used to print data in theserial port of the laptop, the information

are sended to serial port. The sign of rssi is negative when a message is detected, so, it is ignored in the printed process.

Each time print_rssi is called, the samplesis disposed in the following way: [rssi lqi].

uint16_t ch=1, counter,num_received=0;mrfiPacket_t packet;

void print_rssi(int8_t rssi, uint8_t mensaje_tx ){ char output[] = {" 000 000 "}; if (rssi<0) { output[0]=’−’; rssi=−rssi; } output[1] = ’0’+((rssi/100)%10); output[2] = ’0’+((rssi/10)%10); output[3] = ’0’+ (rssi%10); output[5] = ’0’+((mensaje_tx/100)%10); output[6] = ’0’+((mensaje_tx/10)%10); output[7] = ’0’+ (mensaje_tx%10); TXString(output, (sizeof output)−1);}

int main(void){ BSP_Init();

#include #include "mrfi.h"

"radios/family1/mrfi_spi.h"

0x03; P1DIR |=

MRFI_Init(); mrfiSpiWriteReg(CHANNR,195);

UCA0MCTL = UCBRS_2;

P3SEL |=

UCA0CTL1 &= ~UCSWRST; MRFI_WakeUp(); MRFI_RxOn();

0x3; UCA0BR1 = 0x41; UCA0BR0 =

UCA0CTL1 = UCSSEL_2; 0x30;

}

In main funtion different task of configurationare done, firt at all BSP_init and MRFI_init allowed set different values in each register, also different ports are set as output as P1, the channel of transmission is now 195 and some settings of serial port are configured.

(a) First part of the algorithm (functions: main() and print rssi)

MRFI_Receive(&packet); TXString("\n",1); if (num_received==184) { TXString("\n",1);

MRFI_Init(); if (ch==0) {

mrfiSpiWriteReg(CHANNR,195); TXString("Ch 195\n",7); num_received=0; } if (ch==1) { mrfiSpiWriteReg(CHANNR,200); TXString("Ch 200\n",7); num_received=0; } if (ch==2) {

mrfiSpiWriteReg(CHANNR,190); TXString("Ch 190\n",7); ch=−1; num_received=183; } ch++; MRFI_WakeUp(); MRFI_RxOn(); } else { num_received++; tag[0]=’ ’; tag[1]=packet.frame[49]; tag[2]=packet.frame[50]; for(i=9;i<49;i+=2){ rssi= packet.frame[i]; lqi = packet.frame[i+1]; print_rssi(rssi,lqi); } } }

P1OUT ^= 0x01;

P1OUT ^= 0x01;

void MRFI_RxCompleteISR(){ int8_t rssi,lqi,i; char tag[] = {" "};

MRFI_RxIdle();

Each time a packet is received, a line in blank is printed (TXString \n )

When 184 samples are received, another lineis printed, it mark the end of a set of measures.

Each time a channel is switched a flag with the name of this channel is printed.

Basically the whole operation is based on thenumber of samples measured in each channel, once enough information is received, this devicechange its configuration to be able to receive in other channel (between ch 190 and ch 200) by using the functions: MRFI_RxIdle, MRFI_Init andmrfiWriteReg(CHANNR,channel).

If the counter num_received is less than 184 then the information of the packet received is printed, first at all a tag with the identification of device isprinted in this case is letter A or B followed by the number of the base station (1−4).

(b) Rutine used once a message is received

Figure 3.11: Algorithm used in the gateway


Chapter 4

Experimental Results

In the last chapter the basic elements of a location system based on a multipurpose RF platform was

selected. The ez430-RF2500 development tool was chosen due its many desired features for this

application, its low latency and widely configurable elements are settings that cannot be ignored, for

example, it is possible to design almost the whole communication system, modifying all kind of its

features, like the type of modulation, bandwidth, transmission channels (within the range of 2.4GHz

to 2.485GHz) and even the number of layers of the communication protocol (in this case simpliciTI).

In comparison to the waspmote platform which although having a modular architecture, it cannot

compete with this communication aspects (as was illustrated in section 3.1).

The highly integration of the ez430-RF2500 impose some constrains, being a low cost and power

consumption technology its complementary elements (antenna, oscillators, etc) have only the accuracy

and precision necessary to cope with its specifications, reducing the number of techniques that can be

implemented, as was illustrated when the feasibility of the different location techniques where studied

(see more details in section 3.2).

Once decided which development tool will be used, and also the measurement parameters and

the way in which data will be collected, it is necessary to stablish how this information is related to

distance. In this chapter the selection of the estimation technique implemented is presented. The per-

formance of two approaches (propagation model and fingerprinting) through a series of experimental

tests carried out on real environments, are presented.

44 Experimental Results

4.1 Positioning System based on The TI ez430-RF2500 Transceivers

Before talking about the different approaches implemented to estimate the target position, a review of

the main features of the positioning system proposed is presented:

Measurement Parameters The measurement of diagnostic parameters of radio transceivers used

during the demodulation process, such as RSSI and LQI, were choosen as the basis for this system.

Therefore, the whole scheme is completely based on the communication system of reception and

transmission of packets.

These parameters are represented by digital values extracted from the status registers and included

by the drivers in packet.rxMetrics as part of mrfiPacket t, this structure keeps a record of the statistics

on the last received packet, where the first byte is the RSSI and the last seven bits represent the LQI

indicator.

Infrastructure The system proposed has a centralized structure where the control unit is mainly

responsible for data processing. This system has four base stations and two transceivers that act as

the mobile target. Each device operates in the 2.4GHz license-free ISM (Industrial, Scientific and

Medical) band and it is continuously transmitting or receiving messages depending on their usage.

The base stations are equipped with a battery and target board so they can be easily arranged in

order to enhance the in-building coverage, their mainly function is to transmit continuously a message

at a rate of 3.2ms. The mobile device is composed by two transceivers arranged back-to-back which

perform the same task, they are continuously measuring and counting the number of RSSI and LQI

values collected, once they get an enough quantity (200 packets), each of them sends them to the

control unit which counts these messages and depending on the number received, stop their operation.

Data Collection Data collection is an important phase in this system, the design of an entirely

protocol of communication between the different elements of the system was done, more details can

be found in the last chapter where the description of the state machine is presented, in the following

paragraphs the basic procedure to collect information is presented.

Procedure

1. Each base station sends a message of 40 bytes, allowing an indirect measure of the RSSI and

LQI (this information is obtained during the demodulation process).

2. The mobile device receives these messages and starts saving the RSSI and LQI values; when

two hundred data have been saved, a lecture of the data from the next base station takes place,

this switch is constantly repeated until the number of data programmed is recorded.

4.2 Experimental Testbed 45

3. Each time that two hundred data are collected, they are ready to be sent, then the transceiver

changes its operation mode and packages the information on sets of forty bytes to be transmited

in a sequential way to the control unit.

4. Finally the control unit reads these messages and extracts the desired information, this operation

is continuously repeated until the whole data are saved.

Data Processing This is the last stage where the estimation of the target position is obtained. Con-

sidering that this is based on the processing of the signal strength, two techniques were evaluated:

the propagation model and a fingerprinting approach employing neural networks. In the next sections

further analysis will be used to found the best technique for this implementation.

4.2 Experimental Testbed

In order to ensure a proper selection of the localization system, field tests over a real indoors en-

vironment were done. The place has a coverage area of 26.98m2 and dimensions of 7m x 3.86m,

as is shown in figure 4.1(a) and the features depicted in figure 4.1(b). Therefore, this experimental

testbed constitutes a valid representation of the real conditions that indoor location systems affords.

RF signals were exposed to different obstacles, which being made from different materials like wood,

plastics or alloys are a real challenge.

Deployment of the System Four base station were disposed at the locations indicated in figure

4.1(c), each of them was placed at two meters of high in order to reduce the possible obstacles between

transmitters and receptors. The mobile device exposed to this environment, reads periodically the

RSSI and LQI of the different messages received. Each base station is working at different channel so

the mobile devices are continuously changing their configuration settings.


(a) Dimensions of the Testbed

(b) Layout (c) Deployment of the System

Figure 4.1: Experimental SetUp

4.3 Radio Propagation Model 47

4.3 Radio Propagation Model

The propagation model is an experimental approximation of Frii’s model that includes some random

values, the equation 2.1 is used in some proposals as the base of its location system. This is because the

use of this mathematical model reduces the dependency on empirical data, being a technique based on

comparison between real values and theoretically-computed data. However, this approach is directly

impacted by the accuracy of the model proposed, then an stage of parameters configuration cannot be

ignored.

Therefore, the pattern expected is an arrangement of data that describe an exponential attenuation

as is illustrated in figure 4.2.

RSSI

distance

Figure 4.2: RSSI as a function of distance.

In order to examine if the propagation model (equation 2.1) can be used as an alternative solution,

an experiment was designed, it involves the measure of the RSSI values at different distances in order

to determine if its behaviour approaches a predictable model.

Overview of the Experiment: Two transceiver were used to transmit and receive packets at different

distances, they are arranged to ensure line of sight and also the least number of perturbations.

Figure 4.3 depicts the typical attenuation pattern for the ez430-RF2500 development tool, at first

sight the wide variations between different measures taken at the same distance seems to be unavoid-

able, many factors can be attributing to this behaviour such as the propagation effects in real envi-

ronments and the errors produced by the non-ideal elements of these transceivers. This uncertainness

forces to do a further processing in order to achieve an unique solution.

4.3.1 Source of statistical dispersion

Once RSSI values are collected at an specific distance, statistical measures are used to deal with the

uncertainness of this set of measurements. The traditional approach is to calculate the first statistical

momentum, the arithmetic means, which from the viewpoint of digital signal processing represents a


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rssi

Figure 4.3: RSSI as function of distance

filter operation, the moving average filter.

Moving Average Filter

The Moving Average filter is a commonly used filter, which can be defined by the expression 4.1:

y[i] =1

M

M−1∑j=0

(x[i+ j]) (4.1)

Figure 4.5 depicts the filter operation executed during the procedure to measure the RSSI values

of the packet received at a particular position. If the propagation model is taken as valid, then in an

specific position, a constant value of signal strength must be measured (figure 4.4), and this value

will be continuously read by the transceiver at its operation rate (23.2ms). Therefore, if this data are

collected by time windows, it is equivalent to do the convolution between the input signal (a fixed

value of signal strength in the model) with a rectangular pulse (determines by the number of samples

collected).


RSSIT=23.2ms

RSSI

Sampling Time

tt

Figure 4.4: Measuring the RSSI at fixed point

(c)

t

M+1

Fs Fs

M+1

1/N

N

t

(b)

(a)

t

Wm

Figure 4.5: Filtering Process: (a) Signal in time domain; (b) Effect of impulse train; (c) FrequencyResponse

4.3.2 Evaluating the Propagation Model Approach

In the next sections some tests were designed to validate this model in this harsh conditions.


Antenna’s Effect

In order to analyze the effect of anisotropic radiation patterns in the transceivers antennas two transceivers

were used to transmit and receive packets at different points as it is illustrated in the figure 4.6. Four

different sets of positions were analysed, this points defined on figure 4.6(b) form a trayectory that is

useful to analize how the attenuation pattern changes when a trasceiver is far from the source (base

stations).

These sets are defined as follow:

* Set 1: {1,5,9,13,17,21,25,29,33}.

* Set 2: {2,6,10,14,18,22,26,30,34}

* Set 3: {3,7,11,15,19,23,27,31,35}

* Set 4: {4,8,12,16,20,24,26,32,36}

(a) Dimensions (b) Deployment of the System

Figure 4.6: Experimental Testbed


Procedure

* Using the protocols proposed in section 3 but employing only one base station as is shown

on figure 4.6, measurements of the RSSI were taken on each of these points. The number

of samples recollected on each point were around 400, and they were used to describe the

attenuation present when the mobile device (integrated by two sensors) is getting far from the

base station (figures [4.7-4.10]).

* The RSSI values recollected on transceivers A and B will be used to analize how sensible is the

antenna used in this development tool to changes of different change of position.

* And also variations on angle of reception where used to see if the pattern change in a wide range

when this condition are presented.

Results

* The sets of points choosen, gives information about how the radiation pattern influences the

whole behaviour of the system, this issue were not taken into account when the proposed math-

ematical model is used, it only estimate the attenuation of the signal strength in one dimension

(relate the RSSI with the distance between receptor and transmitter).

* As illustrate in figures [4.7-4.10], even though the conditions of this test was almost ideal and

the distances between motes is almost the same for transceiver A and B, the attenuation pattern

describe in those pictures vary in most aspects: analysing trajectory 1 (4.7), measures over

transceiver A oscillate between -75dBm to -50dBm while in B the range is about -80dBm to -

65dBm; Also in some case this pattern is not even close to the ideal, suddenly changes on RSSI

can only be attributed to the propagations effects on indoor environments.


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(a) RSSI over trajectory 1-33 using transceiver A

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(b) RSSI over trajectory 1-33 using transceiver B

Figure 4.7: Effect of antenna’s anisotropy on RSSI measures - Test 1


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Line of Sight (LOS) vs Crowded Environments

The last experiments were done in environments without any obstacles between transmitter and re-

ceptors, this ideal condition is not a valid representation of real indoor environments, where all kind

of objects can obstruct the transmission path. In figure 4.11 the fading pattern at different distances

is shown, in this case the testbed used was the crowded one describe in section 4.2, as can be seen

in environments with LOS the model for this hardware still can be used but with non-LOS condition

this pattern cannot be modeled as an exponential fading. Then, at least for this hardware this is not a

feasible option.

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Figure 4.11: Attenuation Pattern in a Crowded Place

4.4 Fingerprinting

Radio propagation of electromagnetic waves is an unpredictable phenomena, affected by a wide vari-

ety of factors: signal fading is highly influenced by the materials at which signals impinge, depending

on the electric properties of these structures, different amounts of the impinging wave will be re-

flected, diffracted or transmitted. In addition, the processes of transmission and reception have also

an uncertainness degree of precision, the transmitter and receiver channel have associated an additive

noise [42]; and the distortion produced by the inaccuracy of the supported elements like antennas or

oscillators, makes this phenomena hard to approximate using a direct function.

These unavoidable problems produce a very unique pattern of variation that can be modeled by the

4.4 Fingerprinting 57

use of a recursive algorithm like neural networks, which have been found to be very useful to perform

the tasks of approximation, identification and recognition in nonlinear systems [43].

4.4.1 Artificial Neural Networks

This is a recursive algorithm that varies depending on the architecture used, but conserves the same

philosophy, using a set of data, trying to adjust its weights to obtain a function that can predict or

”learn” by this training.

This algorithm is divided into two stages, one of them is a training stage (off-line performance) in

which the different weights are modified in order to minimize an error function which varies depending

on the training algorithm implemented. The other one, is the online performance in which the network

is used to make estimations.

Features of the Neural Network Approach

Architecture: A Multilayer Neural Network was choosen to estimate the target position, this feed-

forward network, has one hidden layer of twenty neurons with sigmoid activation functions (tansig),

followed by a linear output. In this architecture the multiple layers of non linear functions allowed

nonlinear and linear representations between input and output vectors, however there are no commu-

nications in the same layer, then information flow in only one direction.

Training algorithm: The network was trained with the Levenberg-Marquardt backpropagation al-

gorithm (trainlm), an algorithm that basically uses the chi-squared error criterion to minimize a mul-

tivariate function [44, 45].

Basic Procedure:

* On-Line Operation: During the online performance, the input units receive data and broadcast

them to the hidden neurons, which using their activation functions compute their responses and

send them to the output, where a linear transformation takes place, producing the response of

the network to the input pattern.

* Training: During training, an inverse direction of propagation takes place, each output compares

its response with its target value, then, the error is distributed from the output back to all the

units in the previous layer to update the weights in this path.

Implementation of the Neural Network

The design of the neural network can be done using MATLAB, which has an interactive environment

that allows the design of different kinds of networks which can be specified with the comands nntool


or nftool to more specific problems (fitting problems). All kinds of variables can be set: network

architecture, training algorithm, the network size, the number of inputs and outputs, etc.

Since, this approach requires a previous characterization of the place where the system is working,

then, in order to evaluate the real performance of the neural network approach, a Multilayer Neural

Network was used in the harsh environment described in section 4.2.

In the following paragraphs the different stages of this approach will be describe.

Off-Line Stage In this stage, a series of training vectors are required, then in this case a set of

inputs-output patterns are provided in order to adjust the weights in each layer.

* Input Vector: The input vector in this application is composed by the RSSI and LQI values,

measured from the signals generated by each base station, as a consequence the mobile device

will be able to collect sixteen different data, provided by the different measurements on each

base station done by the transceivers A and B.

* Output Vector: It contains each position where data were collected. In this case the positions

correspond to the points depicted as points 1-21 in the testbed 4.2 .

* Procedure: For this test 1000 data were collected on each point, then this data were divide into

three sets: validation, training and testing. Therefore, considering that this test was realized in

21 points, 14700 data were used in the training phase and 6210 were used in the validation and

testing.

ResultsOnce the features of the neural network have been specified, the training process took place, the

training algorithm, in this case the Levenberg-Marquardt backpropagation algorithm, was used to

achieve an optimal approximation. In table 4.1 a summary of the mean square error obtained for each

set of data is presented.

Data SamplesMean Square Error

MSETraining 14700 0.339154

Validation 3105 0.49777Test 3105 0.59765

Table 4.1: Results of The Neural Network Training

4.4 Fingerprinting 59

Additional Tests

When data were collected, a set of this information was reserved to do additional tests, with these

measurements the network was again evaluated in order to ensure a proper operation. In figures 4.13,

4.12 the estimations done by the network on a random points are depicted. This tests illustrates how

the nonlinear function modeled with this network is able to give a reasonable good estimation (the

maximum error in most cases inside around 40cm), the mean square value were used as the statistical

measure to validate the performance of this network. In tables 4.2 and 4.3 a summary with this values

is shown.

PositionMean Square Error

cm1 131.026 15.25169 14.103210 6.6325311 3.322612 7.4039114 4.5389215 10.3211

Table 4.2: Results of Neural Network Validation

PositionMean Square Error

cm2 5.779143 12.42064 30.53325 5.719668 3.4647513 32.0305

Table 4.3: Results of Neural Network Validation

However, is too early to ensure that this system is faithful, the performance of the mobile device is

highly affected by the condition imposed by the antenna, this element is a limiting factor that affect the

behaviour of the network, which depends on the reliable measures to ensure a proper characterization

of the environment (training phase). However if this constrain can be overcome, this network can be

used as a practical solution in the development of a positioning system based on a commercial off the

shelf development tool, which means the reduction on the costs of positioning systems.


0 50 100 150 200 250 300 350

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16 17

18

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MSE = 131.024

MSE = 15.2516

MSE = 14.1032 MSE = 6.63253

MSE = 7.40391

MSE = 3.38226

MSE = 4.53892

MSE = 10.3211

Figure 4.12: Neural Network Estimations on Different Points

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MSE = 5.71966

MSE = 5.77914MSE = 12.4206

MSE = 30.5332

MSE = 3.46475

MSE = 7.31595

MSE = 4.53154

MSE = 32.0305

Figure 4.13: Neural Network Estimations on Different Points

Chapter 5

Conclusions and Future Work

5.1 Contributions

This thesis was addressed to tackle the problem of localization and tracking using commonly avail-

able transceivers as the TI ez430-RF2500 development tool. This tool, being a low cost, low power

platform, constitutes an attractive solution, the designing of the whole architecture and the elements

necessary to integrate a feasible system were considered, different tests were carried out over prelim-

inary systems allowing to enclose and select the different methodologies adopted.

Through the different settings evaluated and also the different tests performed, it is possible to

withdraw the following conclusions:

* The designing of a location system based on a multipurpose development tool is a challenging

problem which requires a deep knowledge of the hardware constrains. In this case, the main

constrain on the use of a custom hardware was the high intrinsic latency of the devices, which

constitutes the most important constrain, limiting the real time performance of the whole system.

* The use of multipurpose systems have to be supported by an excellent communication system,

because this is the key element in any wireless localization system.

* The use of the RSSI and LQI measurements, have proven to be an stronger tool that, being avail-

able in most radio transceiver, enable the scalability of localization systems to any architecture.

* Techniques as TDOA and Interferometry based on low cost hardware are strongly limited by

the features of the hardware. This is the case of the Pinpoint devices, where the use of TDOA

is limited to the use of systems with a medium access control clock stamping (to get responses

at a time in the order of microseconds) and also the Interferometer approach requires a high

precision in the calibration of the carrier frequency in order to get unmodulated waves separated

by a few hertz.

62 Conclusions and Future Work

* The propagation model as a predictor its not a feasible option in this scheme where different

aspects like transmitting power, hardware constrains and environment features greatly influence

the measurements.

* The inability to model the noise produced by radio propagations and also by the inaccuracy of

transceivers, encourages the use of neural networks that even thought needs an off-line calibra-

tion, guarantee a proper response under harsh condition.

* The use of neural networks give some advantages because they can model a multidimensional

problem, based on non-linear relationships in a relative easy way, making the system easily

implementable under any condition, the prior knowledge of the noise contributions or the dis-

position of the different elements of the system are not relevant, the off-line stage allowed to

model the propagation patterns obtained from the measurements of the RSSI and LQI generated

by the signal on each base station.

5.2 Future Work

The main constrain of a location system based on a multipurpose development tool as the TI ez430-

RF2500 was the anisotropy of its antenna which produce an irregular pattern on measurements depend-

ing on its orientations at the same position. Therefore, the use of an omnidirectional antenna instead

of the custom antenna provided on the ez430-RF2500 kit, could resolve most problems concerned

with the lack of precision on measurements and improving the robustness of the system. However,

this antenna has to be carefully designed because any mismatch on its size can change the electric

properties of this element and produce undesired effects.

In addition, although this thesis has illustrated that the use of neural networks is the most promising

proposal which allows to cope with the harsh conditions imposed by the multiples inaccuracies of

hardware elements and also by the layout of the coverage area, is necessary to study more algorithms

in order to guarantee lower computational requirements.

Bibliography

[1] B. Giussani, “More cell phones than peaple,” Jun. 2006. [Online]. Available: http:

//www.lunchoverip.com/2006/06/more cell phone.html

[2] T. Rappaport et al., Wireless communications: principles and practice. Prentice Hall PTR New

Jersey, 1996, vol. 207.

[3] J. Hightower and G. Borriello, “Location systems for ubiquitous computing,” Computer, vol. 34,

no. 8, pp. 57 –66, aug 2001.

[4] F. Zhao and L. Guibas, Wireless sensor networks: an information processing approach. Morgan

Kaufmann Pub, 2004.

[5] M. Mah, N. Gupta, and A. Agrawala, “Pinpoint time difference of arrival for unsynchronized

802.11 wireless cards,” in INFOCOM, 2010 Proceedings IEEE, march 2010, pp. 1 –5.

[6] S. Lanzisera, D. Lin, and K. Pister, “Rf time of flight ranging for wireless sensor network lo-

calization,” in Intelligent Solutions in Embedded Systems, 2006 International Workshop on, june

2006, pp. 1 –12.

[7] P. Bahl and V. Padmanabhan, “Radar: an in-building rf-based user location and tracking system,”

in INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communi-

cations Societies. Proceedings. IEEE, vol. 2, 2000, pp. 775 –784 vol.2.

[8] U. Ahmad, A. Gavrilov, U. Nasir, M. Iqbal, S. J. Cho, and S. Lee, “In-building localization using

neural networks,” in Engineering of Intelligent Systems, 2006 IEEE International Conference

on, 0-0 2006, pp. 1 –6.

[9] A. Awad, T. Frunzke, and F. Dressler, “Adaptive distance estimation and localization in wsn

using rssi measures,” in Digital System Design Architectures, Methods and Tools, 2007. DSD

2007. 10th Euromicro Conference on, aug. 2007, pp. 471 –478.

[10] I. Amundson, J. Sallai, X. Koutsoukos, and A. Ledeczi, “Radio interferometric angle of arrival

estimation,” in Wireless Sensor Networks, ser. Lecture Notes in Computer Science, J. Silva,

http://www.lunchoverip.com/2006/06/more_cell_phone.html

http://www.lunchoverip.com/2006/06/more_cell_phone.html

64 BIBLIOGRAPHY

B. Krishnamachari, and F. Boavida, Eds. Springer Berlin / Heidelberg, 2010, vol. 5970, pp.

1–16.

[11] J. Sallai, A. Ledeczi, I. Amundson, X. Koutsoukos, and M. Maroti, “Using rf received phase for

indoor tracking,” in HotEmNets, Killarney, Ireland, 06/28/2010 2010.

[12] A. Bensky, Short-range wireless communication: fundamentals of RF system design and appli-

cation. Newnes, 2004, vol. 1.

[13] O. C. Laboratory, “Electromagnetic radiation: Field memo,” May 1990. [Online].

Available: http://www.osha.gov/SLTC/radiofrequencyradiation/electromagnetic fieldmemo/

electromagnetic.html

[14] R. O.Duda, “The physics of sound.”

[15] K. Curran, E. Furey, T. Lunney, J. Santos, D. Woods, and A. McCaughey, “An evaluation of

indoor location determination technologies,” Journal of Location Based Services, vol. 5, no. 2,

pp. 61–78, 2011.

[16] J. Hightower, R. Want, and G. Borriello, “Spoton: An indoor 3d location sensing technology

based on rf signal strength,” UW CSE 00-02-02, University of Washington, Department of Com-

puter Science and Engineering, Seattle, WA, 2000.

[17] H. Lim, L.-C. Kung, J. C. Hou, and H. Luo, “Zero-configuration, robust indoor localization:

Theory and experimentation,” in INFOCOM 2006. 25th IEEE International Conference on Com-

puter Communications. Proceedings, april 2006, pp. 1 –12.

[18] T. King, S. Kopf, T. Haenselmann, C. Lubberger, and W. Effelsberg, “Compass: A probabilistic

indoor positioning system based on 802.11 and digital compasses,” in Proceedings of the 1st in-

ternational workshop on Wireless network testbeds, experimental evaluation & characterization,

ser. WiNTECH ’06. New York, NY, USA: ACM, 2006, pp. 34–40.

[19] I. C. Paschalidis, K. Li, and D. Guo, “Model-free probabilistic localization of wireless sensor

network nodes in indoor environments,” in Proceedings of the 2nd international conference on

Mobile entity localization and tracking in GPS-less environments, ser. MELT’09. Berlin, Hei-

delberg: Springer-Verlag, 2009, pp. 66–78.

[20] N. Irfan, M. Bolic, M. Yagoub, and V. Narasimhan, “Neural-based approach for localization

of sensors in indoor environment,” Telecommunication Systems, vol. 44, pp. 149–158, 2010,

10.1007/s11235-009-9223-4.

http://www.osha.gov/SLTC/radiofrequencyradiation/electromagnetic_fieldmemo/electromagnetic.html

http://www.osha.gov/SLTC/radiofrequencyradiation/electromagnetic_fieldmemo/electromagnetic.html

BIBLIOGRAPHY 65

[21] E. Furey, K. Curran, and P. Mc Kevitt, “Habits: A bayesian filter approach to indoor tracking

and location,” in Artificial Intelligence and Cognitive Science, 2011 22nd Irish Conference on,

aug. 2011, pp. 11 –25.

[22] T. Watteyne, “ezwsn-exploring wireless sensor networking lab version,” 2009.

[23] T. Instruments, “Cc2500: Low-cost low-power 2.4 ghz rf transceiver,” SWRS040C, Datenblatt,

2009.

[24] L. Friedman, “Simpliciti: Simple modular rf network specification.”

[25] L. Lemaitre, “A brief tutorial on simpliciti 1.1.1.”

[26] “Waspmote support,” Jun. 2011. [Online]. Available: http://www.libelium.com/api/waspmote/

[27] H.-l. Chang, J.-b. Tian, T.-T. Lai, H.-H. Chu, and P. Huang, “Spinning beacons for precise indoor

localization,” in Proceedings of the 6th ACM conference on Embedded network sensor systems,

ser. SenSys ’08. New York, NY, USA: ACM, 2008, pp. 127–140.

[28] K. Romer, “The lighthouse location system for smart dust,” in Proceedings of the 1st interna-

tional conference on Mobile systems, applications and services, ser. MobiSys ’03. New York,

NY, USA: ACM, 2003, pp. 15–30.

[29] A. Nasipuri and R. el Najjar, “Experimental evaluation of an angle based indoor localization

system,” in Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2006 4th

International Symposium on, april 2006, pp. 1 – 9.

[30] J. Friedman, Z. Charbiwala, T. Schmid, Y. Cho, and M. Srivastava, “Angle-of-arrival assisted

radio interferometry (ari) target localization,” in Military Communications Conference, 2008.

MILCOM 2008. IEEE, nov. 2008, pp. 1 –7.

[31] S. Namtvedt, “Rssi interpretation and timing,” Texas Instruments Design Note DN505,

SWRA114B.

[32] G. Mao, B. Fidan, and B. Anderson, “Wireless sensor network localization techniques,” Com-

puter Networks, vol. 51, no. 10, pp. 2529–2553, 2007.

[33] A. Cope and M. Jorgenson, “Overview of location technologies.”

[34] I. Cisco Systems, Enterprise Mobility 3.0 Design Guide, 2007.

[35] S. Schwarzer, M. Vossiek, M. Pichler, and A. Stelzer, “Precise distance measurement with ieee

802.15.4 (zigbee) devices,” in Radio and Wireless Symposium, 2008 IEEE, jan. 2008, pp. 779

–782.

http://www.libelium.com/api/waspmote/

66 BIBLIOGRAPHY

[36] M. Maroti, P. Volgyesi, S. Dora, B. Kusy, A. Nadas, A. Ledeczi, G. Balogh, and K. Molnar,

“Radio interferometric geolocation,” in Proceedings of the 3rd international conference on Em-

bedded networked sensor systems, ser. SenSys ’05. New York, NY, USA: ACM, 2005, pp.

1–12.

[37] B. Kusy, I. Amundson, J. Sallai, P. Volgyesi, A. Ledeczi, and X. Koutsoukos, “Rf doppler shift-

based mobile sensor tracking and navigation,” ACM Trans. Sen. Netw., vol. 7, pp. 1:1–1:32,

August 2010.

[38] H. Sverre, “Cc2500 and cc2510/cc2511 sensitivity versus frequency offset and crystal accuracy,”

Design Note DN021.

[39] H. Khaleel, C. Pastrone, F. Penna, M. Spirito, and R. Garello, “Impact of wi-fi traffic on the

ieee 802.15.4 channels occupation in indoor environments,” in Electromagnetics in Advanced

Applications, 2009. ICEAA ’09. International Conference on, sept. 2009, pp. 1042 –1045.

[40] S. Petersen and S. Carlsen, “Performance evaluation of wirelesshart for factory automation,” in

Emerging Technologies Factory Automation, 2009. ETFA 2009. IEEE Conference on, sept. 2009,

pp. 1 –9.

[41] W. E. eiSos GmbH & Co.KG, “Antenna multilayer 2.4-2.5ghz we-mca 7488910245,”

1.7488910245. [Online]. Available: http://elcodis.com/parts/1750916/7488910245.html

[42] L. Badri, “Development of neural networks for noise reduction,” 2009.

[43] L. M. Seijas and E. C. Segura, “A hybrid neural network model for pattern recognition: Solving

ambiguities and explaining answers.”

[44] G. Henry, “The levenberg-marquardt method for nonlinear least squares curve-fitting problems,”

April.

[45] M. Lourakis, “A brief description of the levenberg-marquardt algorithm implemented by levmar,”

matrix, vol. 3, p. 2, 2005.

http://elcodis.com/parts/1750916/7488910245.html

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Design and Implementation of a Localization System Based ... · ABSTRACT TITLE: Design and Implementation of a Localization System Based on a RF Multipurpose Platform AUTHORS: LUZ