Comparisons of Indoor Position Enhancements by Using Mean and Kalman Filtering Techniques

JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 11, ISSUE 2, FEBRUARY 2012

9

© 2012 JCSE

www.journalcse.co.uk

Comparisons of Indoor Position Enhancements by Using Mean and Kalman

Filtering Techniques

Hakan Koyuncu and Shuang Hua Yang

Abstract - In this paper, a study of two filtering techniques is compared during indoor localization. Linear mean filtering

and Kalman filtering techniques are employed during the pre and post position estimation phases to determine the location

accuracies of unknown objects. Zigbee wireless sensor nodes (WSN) are employed together with RF Link quality indicator

(LQI) values in measurements. Fingerprint based localization technique is utilized and k-NN algorithms are used to

calculate the unknown positions. Linear mean filtering gives an average position accuracy of 3.5 meters in a sensing area

with a grid space of 4 meters. Kalman filtering, on the other hand, gives an average position accuracy of 4.5 meters in the

same sensing area.

Index Terms- WSN, RF, LQI, k-NN, Transmitter (Tx), Receiver (Rx), Kalman, Application program (AP).

1 INTRODUCTION

The localization problem has received considerable

attention in the area of wireless sensor nodes and

computing. Radio frequency (RF) technology is a

widely used technology which utilizes received

signal strength indicator (RSSI) or Link Quality

indicator (LQI) to find the object positions. Although,

in theory, they are a function of distance between a

transmitter and a receiver, in practice, there are many

problems such as reflections and absorptions due to

propagation media [1,2].

In RF based localization, there are basically 2

models, [3]. In the first model, a transmitter which is

a WSN is placed on the object and the receivers at

well known points receive transmissions from this

transmitter. The location of the object is computed by

using these transmissions on a server computer. In

the second model, a number of WSN transmitters are

deployed at known positions and a receiver on the

unknown object receives RSSI or LQI information to

compute the object position.

This study will focus on the second model in which

the unknown object will carry an active WSN

Receiver. In the envisaged system, four WSN

transmitters, one Receiver, and middleware software

are utilized.

H.Koyuncu is with Computer Science Dept, Loughborough

University,Loughborough UK.

S.H.Yang is with Computer Science Dept, Loughborough

University, Loughborough, UK.

Location fingerprinting technique is applied to find

the unknown object locations from a set of radio

location fingerprints collected in a 2D space,[4].

Initially a fingerprint database is generated by

recording LQI values at every grid point of the

sensing area. k-NN algorithm is utilized to estimate

the location of the unknown object. This algorithm

computes the Euclidean distance between the

measured LQI vector at unknown point and LQI

vector at each fingerprint in the database. The

coordinates, associated with the fingerprint, which

provide the smallest Euclidean distance is returned as

the estimate of the unknown position.

Linear mean filtering and Kalman filtering are

introduced in two phases identified as pre-estimation

and post-estimation phases. During pre-estimation

phase these filtering are used to reduce the random

effects of the received LQI data. During the post-

estimation phase they are used to reduce the

variations of calculated unknown object coordinates.

Linear mean filtering is deployed on the received

LQI values by obtaining their mean value at every

fingerprint point in pre-estimation phase. Kalman

filtering is similarly deployed on the received LQI

values at every fingerprint point in this phase [5-7].

Fingerprint database is divided into a number of

sequential sub fingerprint databases and the unknown

object coordinates are calculated with respect to each

of these newly created databases. Linear mean


10

© 2012 JCSE


filtering and Kalman filtering are deployed on the

calculated object coordinates with these sub

fingerprint databases during the post-estimation

phase.

In the literature, Linear mean and Kalman filtering

techniques are applied individually on the received

RSSI data to smooth the unpredicted variations

before localization algorithms are used. Challenging

aspect of this study is to deploy both filtering

techniques and compare their localization accuracy

levels with respect to grid space of the sensing area

before and after the position calculation. The work is

extended to the generation of sub fingerprint

databases and multiple numbers of object coordinates

are calculated for one unknown object point.

This paper is organized as follows. In section 2, an

understanding of fingerprint localization and k_NN

algorithm together with the weight function used in

the calculations are presented. In section 3,

methodology of linear mean filtering and Kalman

filtering techniques are introduced. Implementations

of them are carried out in section 5. In section 6,

conclusions are presented.

2 FINGERPRINT LOCALIZATION

Received signal strength indicator (RSSI) is a

parameter to identify the incoming radio signal. [1].

In Wireless sensor networks, the received signal

strength is converted to RSSI. But in many

applications RSSI has a high variance due to

interference and environmental factors and the

localization of unknown node becomes very

imprecise.

Another kind of radio signal identification is

described by LQI values. The determination of

distances is based on LQI of the transmission

between transmitters and receivers. According to

IEEE 802.15.4 standards, LQI is identified as the

strength of the received signal. It is proportional to

RSSI and has a discrete value between 0 and 255.

Hence RSSI can be directly mapped into LQI, [8].

The transmitter nodes transmit the signal packets and

receiver mobile node logs the LQI of the incoming

signal packets and sends them to PC. LQI values of

incoming radio signals decrease with increasing

distance “d” between transmitters and receivers in

free space, [9].

If the indoor sensing area does not have any

obstacles, the radio propagation can be considered

similar to the one in free space for small distances.

On the other hand, if there are any obstacles, they

introduce attenuation factors in radio propagation

hence a variation in LQI values.

3 MATHEMATICAL MODEL

In the study, 4 WSN transmitters at the corners and

one receiver on the unknown object are employed in

the sensing area. See figure 1.

Figure 1: Sensing area showing a grid point G and

an unknown point P

Transmitter Bi where i=1,2,3,4 at the known positions

transmit their LQI values to the receiver on the

unknown object P.

The signal distance between P and the grid points in

the fingerprint database is identified as Euclidean

distance and calculated by using LQI values recorded

at the respective positions, [4]

The grid points with the smallest Euclidean distances

to the unknown location are used to estimate the

location of the unknown object. This estimation

algorithm is called k-nearest neighborhood algorithm

(k-NN).

To improve the accuracy of the estimation, weighting

technique is employed in the calculations. wi weight

factor of the ith neighboring grid point in k-nearest

neighborhood is utilized as in equation (1) . See

reference [4].

k

i i

ii

E

Ew

13

3

1

1

(1)

4 METHODOLOGY

In the study, location fingerprinting technique is

utilized by using k-NN algorithm and weight factor

algorithm in equation (1). The technique is extended

with smoothing algorithms applied on the received


11

© 2012 JCSE


LQI values in order to improve the position detection

accuracy. The radio propagation characteristics

display a random behavior. For example, fingerprints

of two close points maybe very similar or very

different from each other in time domain. These finite

random variations are considered as noise and

filtering techniques are applied to reduce them.

Filtering techniques are deployed in localization

problems to improve positioning accuracy. There are

basically two filtering techniques in the literature

which are used in RFID studies. These are linear

mean filtering and Kalman filtering. They are applied

on the received RSSI or LQI data. They reduce the

randomness of the data and help to increase the

accuracy of the position detection. Positioning

calculations are carried out after this data correction

stage. Filtering techniques are applied in two phases

in the calculations. First phase is called pre-

estimation phase and the second phase is called post-

estimation phase. Pre-estimation phase is solely

related to data collection and their processing. Post

estimation phase is the phase where the unknown

object coordinates are calculated.

The novelty in this study is to generate a number of

sub fingerprint databases from each set of LQI

readings at every fingerprint database point. A

number of unknown object coordinates in time

domain is calculated for the same unknown object

coordinate by using sub fingerprint databases. The

variations between these unknown object coordinates

are reduced by using above filtering techniques

which is a new approach in localization.

4.1 Linear mean filtering

Initially, Linear mean filtering is deployed in the pre-

estimation phase. Raw LQI data is processed and its

randomness is reduced in this phase. A stream of LQI

reading is received at every grid point in time

domain. Mean filtering technique is applied on these

LQI values and a final mean value is recorded at that

grid point of fingerprint database as shown in

equation (2). n

i

imean LQIn

LQI1

.1 (2)

where n is the number of LQI recordings at one grid

point. For example, 20 LQIA values from transmitter

B1 is arriving at the receiver on one grid point. The

mean of these 20 values is recorded as the mean

filtered LQI value at that grid point. A new

fingerprint database is generated by using these

filtered LQI values at every grid point and it is used

to estimate the unknown object position as before.

During the post-estimation phase, several fingerprint

databases are generated by using the equal number of

subsections of recorded “n” number of LQI values at

each grid point. Unknown position coordinates are

calculated by using each of the newly generated

fingerprint databases. Same number of unknown

object coordinates, (x,y), are obtained as the number

of fingerprint databases. Linear mean filtering is

applied on these (x,y) coordinates to determine the

best estimate of the unknown position coordinate as

shown in equation (3). m is the number of calculated

unknown object coordinates.

(3)

4.2 Kalman

Filtering

The purpose of the Kalman filtering is to estimate the

state of a system from previous state measurements

which contain random errors. One-dimensional

Kalman filter is appropriately employed on the

randomly received LQI values with respect to time,

[10,11]. A general block diagram is given for the

Kalman filtering operations in Figure 2.

Suppose there is a random variable x(t) and there is a

need to estimate its value at certain times of

t0, t1, t2, t3, t4……

x(tk) satisfies a linear dynamic equation :

)()()1( kutFxtx kk (4)

m

j

m

j

mean ym

xm

yx1 1

1,

1),(


12

© 2012 JCSE


Figure 2: Block diagram for Kalman filtering

where F is a constant number, u(k) is the random

noise with zero mean (u(k) = 0) and variance of

u(k) =Q .

Kalman filter needs an initial estimate to get started.

Initial estimate of x(t0) is the actual value of x at time

t0. x(t0) is called xe and it is the best estimate of x.

The variance of the error becomes;

]))([( 2

0 extxEP (5)

E is the expected value operator, x(t0) is the actual

value of x at time t0 and xe is the best estimate of x.

Hence the term in brackets is the error in the

estimate. In order to estimate x(t1), equation (3) is

used. When k=0; equation (3) becomes;

)0()(.)1()( 001 utxFtxtx (6)

Hence the new best estimate of x(t1) is ;

new xe = F. xe (7)

Variance of the error of this estimate is

]))([( 2

1 enewxtxEnewP (8)

Substitute equations (3) and (4) into (7)

]).)(.[( 2

0 exFutxFEnewP (9)

Equation (8) can be expanded as;

]))(([2))(([( 0

22

0

2 uxtxFEEuxtxFEnewP ee (10)

The last term in equation (10) is zero due to the fact

that u is not correlated with x(t0) and xe . Therefore

equation (10) becomes;

)())(( 22

0

2 uExtxEFnewP e (11)

after substitution , newP becomes;

QFPnewP 2. (12)

Substituting P, F, Q values in equation (12); newP

can be found. This process continued for each x

value and the new estimated xe values are generated

for a stream of x values. Kalman filtering is applied

in both estimation phases.

5 IMPLEMENTATION

JENNIC JN5139 wireless sensor nodes were

deployed in the study. The Zigbee Home Sensor

program was used to program JN5139 active devices

to work as transmitter and receiver respectively.

JN5139 receiver on unknown object is interfaced to a

computer via a USB port for data transfer. ZigBee

protocol which is based on IEEE 802.15.4 standards

is used during communication between transmitter

and receiver nodes,[12].

The layout of the sensing area is an international

basketball field in a sports hall. A rectangular grid

area of 20m x 12m is selected on the floor plane with

a grid distance of 4 meters and any unknown object

location is utilized within this rectangular area.

Transmitter nodes are placed at 4 corners of the

sensing area. LQI values coming from transmitter

nodes are recorded by the object receiver node at

each grid point. There are 24x4=96 LQI entries in the

fingerprint database. Each entry in the database

includes a mapping of the grid coordinate (x,y) and 4

LQI values at that point.

At every grid point, 4 readings of LQI values are

recorded n=20 times. The mean value of n LQI

values is calculated by using equation (2) and

recorded in the database as the mean LQI value at

that grid point for each transmitter. Hence a new

mean valued fingerprint database is generated during

pre-estimation phase.

Several unknown location fingerprint vectors,

P(r1,r2,r3,r4), are measured at different grid points

during the on-line measurement phase. “n” number of

LQI readings are collected at these grid points from

each transmitter. They are mean filtered out to

generate a single mean value of LQI corresponding to

one transmitter. Hence mean filtered P vectors for

different unknown object positions are generated.

By using k-NN and weight factor algorithms together

with mean filtered fingerprint database and mean

filtered unknown object fingerprint vectors, the

unknown positions of the objects are estimated and

displayed in Table 1.

Table 1: Estimated unknown position coordinates

with mean filtered fingerprint database in pre-

estimation phase


13

© 2012 JCSE


In the second part, one dimensional Kalman filtering

is applied in two phases. During pre-estimation

phase, Kalman filtering is applied on “n” LQI values

which are received from each transmitter at every

grid point, [13], and generated a resultant Kalman

filtered LQI value. This value is the predicted LQI

value and it is recorded in a new fingerprint database

at that grid point. A sample plot of LQI values with

and without Kalman Filtering at an example grid

point are shown in Figure 3a and 3b.

Figure 3a: Plots of LQIA, at grid point (0,2),

Black: raw LQI values , Blue: Kalman filtered

LQI values

Figure 3b: Plots of LQIB, at grid point (0,2),

Black: raw LQI values , Blue: Kalman filtered

LQI values

A new fingerprint database is generated by using the

Kalman values at every grid point and identified as

Kalman fingerprint database. Similar Kalman

filtering technique is used to determine the unknown

fingerprint P vectors. n number of LQI values from

each transmitter are recorded at each unknown object

location. Kalman filtering is applied on them and the

Kalman filtered LQI values are generated for these

unknown locations.

By using the generated Kalman fingerprint database

and the Kalman filtered unknown fingerprint P

vectors, Fingerprint localization technique is utilized

to determine the unknown object location

coordinates. Estimated unknown location coordinates

are presented in table 2.


by using Kalman filtering in pre-estimation phase

During post-estimation phase, Linear mean filtering

is applied on the estimated position coordinates.

n=20 LQI readings at each grid point is organized in

groups of m=5 consecutive readings. This grouping

is applied on all LQI readings and 4 sub fingerprint

databases are generated from the main fingerprint

database.

5 consecutive LQI readings at each grid point are

mean filtered out and placed at that grid point as the

mean LQI value in the new sub fingerprint database.

Unknown object P vectors are similarly processed

and 5 consecutive sub P vectors are generated in

order to be used with respective sub fingerprint

databases.

Estimated unknown object coordinates for one sub

fingerprint database is given in table 3.


with mean filtered fingerprint database in post-

estimation phase

Hence 4 unknown object coordinates are calculated

for every unknown object location in time domain.

These calculated object coordinates are linear mean

filtered by averaging them to generate the coordinates

of unknown object location during post-estimation

phase.

Kalman filtering is applied on the above calculated 4

unknown object coordinates to reduce the variations

among them during post-estimation phase. Examples

of Kalman filtering at one unknown object

coordinates are given in Figures 4 .


14

© 2012 JCSE


Figure 4: Kalman filtering of x,y coordinates at

object point (4,4)

Estimated Kalman filtered unknown object

coordinates with respect to coordinates calculated

with sub fingerprint databases during post-estimation

phase are presented in table 4.

Table 4: Estimated Kalman filtered unknown

object coordinates during post-estimation phase

Finally, Estimated object coordinates with respect to

their estimation methods are summarized in table 5.

Table 5: Comparisons of estimated object

coordinates with respect to estimation methods.

6 CONCLUSIONS

This study presented some novel enhancement

techniques in 2D indoor localization such as linear

mean filtering and Kalman filtering during different

stages of localization. These stages are identified as

pre-estimation and post-estimation phases. Filtering

techniques are applied before and after the estimation

process and the estimated results are compared with

respect to positioning accuracies.

Initially, the raw LQI data is filtered out and large

fluctuations are eliminated. Once the unknown

position coordinates are calculated, the filtering is

applied on these coordinates to reduce their spatial

variations. Filtering raw data can be seen in literature

but the filtering during the post-estimation phase is a

new approach in localization procedures. Another

novel procedure is to divide the main fingerprint

database into sub fingerprint databases in time

domain and calculate the unknown points for all

these sub fingerprint databases.

In the study Zigbee wireless sensor nodes namely

readers and tags are deployed together with radio

location fingerprinting technique. A 2D signal

strength matrix is constructed by using LQI values of

the radio signals and identified as fingerprint

database. Unknown position coordinates are

estimated on a planar and obstacle free test bed.

Fingerprint databases are generated by utilizing linear

mean filtering and Kalman filtering.

Fingerprint localization technique together with k-

NN and weight factor algorithms are employed and

unknown object positions are determined in indoors.

It can be seen in table 5 that the best average error

distance is 3.5meters with mean filtered fingerprint

database. This is less than the grid space of 4meters.

Average error distance increases from using pre-

estimated to post-estimated Kalman fingerprint

databases.

Although the best average error distance is less than

the grid space distance, the error between the actual

and the estimated object coordinates is in 3-4 meter

range.

If the results are related to authors’ previous similar

work in reference, [4], it can be seen that there is a

close relationship between the size of the sensing

area and the position accuracy. Similar fingerprint

localization technique is utilized in both cases. A

sensing area of 5mx3m produced an error distance of

1.1m between the actual and the estimated

coordinates of an unknown object point in reference

[4] while 20mx12m sensing area in this study

produced a minimum error distance of 3.5 m.

In this study, the error distance is less than 1 grid

space. In small sensing area the error distance is

%110 of the grid space while in larger sensing area

the error distance is %88 of the grid space. Small

sensing areas are susceptible to more random

recordings of LQI values due to interference

compared to smaller areas. This factor is reflected on

the error size. This deficiency can be eliminated by

1 1.5 2 2.5 3 3.5 44.5

5

5.5

6

6.5

7

7.5

8

Iteration

LQ

I valu

es

noisy LQI measurements

an estimate of LQI

1 1.5 2 2.5 3 3.5 47.4

7.6

7.8

8

8.2

8.4

8.6

8.8

9

9.2

9.4

Iteration

LQ

I valu

es

noisy LQI measurements

an estimate of LQI


15

© 2012 JCSE


introducing large number of reference receivers in the

sensing areas.

Finally it can be concluded that the accuracy of

position detection is the best with mean filtering. Pre-

estimation phase Kalman filtering results are close to

mean filtering results. In future works, Larger

number of LQI recordings will be carried out and the

effects of the sensing area size and the introduction of

reference receivers will be studied to improve the

positioning accuracies.

REFERENCES

[1] T.S. Rappaport ,”Wireless communications principles

and practise”,Prentice Hall PTR ,1996

[2] H.koyuncu,S.H.Yang” A survey of indoor position-

ing and object locating systems” ,IJCSNS , Vol 10,

No 5, May 2010 , pp 121-128

[3] D,Zhan,J.Ma,Q.Chen and L.M.Ni “An RF based

system for sensing transceiver free objects”, in

proceedings of percom,2007

[4] H.Koyuncu,S.H.Yang” A 2D positioning system using

WSNs in indoor environment”, IJECS-IJENS, Vol 11,

No 3, pp70-77, 2011

[5] Simo Aliloytty,Tommi Perala, Robert Piche;

“Fingerprint Kalman Filter in Indoor Positioning

applications”, 18th IEEE Conf on control applications

July 2009, pp 1678-1683

[6] I Guvenc,Abdallah R. Jordan, O.Dedeoglu; “Enhan-

cements to RSS based indoor tracking systems using

Kalman Filters”, International Signal processing

Conference (ISPC) and Global signal processing

expo, Marc 3 ,2003 , Dallas , USA

[7] Wan Young Chung,Boon Giin Lee, Chi Shian yang;

“3D virtual viewer on mobile device for wireless

Sensor network based RSSI indoor tracking

System”, Sensors and Actuators B : Chemical 140,

2009, pp 35-42

[8] Texas instruments “ Zigbeready RF transceiver”

http://focus.ti.com/lit/ds/swrs041b/swrs041b.pdf,

2007

[9] Ralf grossman “Localization in zigbee-based wireless

sensor networks” Technical report University of

Rostock,Institute MD, April 2007

[10] http://www.math.wustl.edu/~victor/classes/

ma450/ classes/ma450/Kalmanf.m

[11] P.D Joseph, “The one dimensional Kalman Filter,

Introductory lesson”

[12] http://www.jennic.com/jennic_support/application

_notes/jn-an-1052_home_sensor_demonstration_

using_zigbee

[13] Raman Kumar K, Yogesh A Powar, Varsha Apte;

“Improving Accuracy of Wireless LAN based

Location Determination System using Kalman Filter

and Multiple Observers” , Dept. of Computer Science

and Engineering Indian Institute of Technology

Bombay Mumbai, Maharastra, India

Hakan Koyuncu has a Bsc in Computer Engineering and

an Msc in Computer science . He is currently doing his

PHD in Computer Science department of Loughborough

University ,Loughborough,UK . His research area is in

wireless sensor Networks and mobile computing.

Shuan Hua Yang is a Professor in Computer Science

department of Loughborough University ,loughborough,

UK . He is FInstMC, SMIEEE and CEng. His research

areas are in wireless sensor networks ,pervasive computing

,mobile agent Technologies and internet based control.

http://focus.ti.com/lit/ds/swrs041b/swrs041b.pdf

http://www.math.wustl.edu/~victor/classes/%20%20%20%20%20%20%20%20%20%20ma450/%20classes/ma450/Kalmanf.m

http://www.math.wustl.edu/~victor/classes/%20%20%20%20%20%20%20%20%20%20ma450/%20classes/ma450/Kalmanf.m

http://www.jennic.com/jennic_support/application

Documents

Comparisons of Indoor Position Enhancements by Using Mean and Kalman Filtering Techniques