Toward Better Indoor Localization: Cooperative Localization and Estimation Fusion

Toward Better Indoor Localization:Cooperative Localization and Estimation

Fusion

Gary Chan, Associate Professor

The Hong Kong University of Science and Technology

1

Outline

2

Indoor localization techniques Improving accuracy on current localization

infrastructure Cooperative (Peer-to-peer) localization

Collaborative mobile devices Simulation results

Estimation fusion Optimally combining multiple estimations Preliminary results

Conclusion

Indoor Localization Mobile device capabilities and penetration of

wireless access networks Many new types of mobile services become viable

Location based service (LBS) is with great commercial potential

Indoor LBS Find the closest restaurant, the best-buy-of-the-day of a

shop, etc. Better localization

Better service Better routing for correctness and bandwidth efficiency

LBS relies on accurate localization of client devices in order to provide high quality services

3

Challenges of Indoor Localization Global Positioning System (GPS) only works

well outdoor Indoor environment

Complicated layout leads to complex fading, shadowing and interference, affecting its accuracy

Line-of-sight (LoS) not easily achievable indoor Requirements

High accuracy Computationally light-weighted Privacy Etc.

4

Localization Techniques

5

Measurements Distance-based (time of arrival, time difference of arrival,

received signal strength, etc.) Angle-based (Angle of arrival) Pattern-based Motion, velocity and direction Electromagnetic Etc.

Techniques Trilateration (for distances) Triangulation (for angles) Inertial navigation systems (INS) Fingerprinting Optimization etc.

Distance-based techniques

Measure distances among nodes and infrastructure nodes/landmarks

Use mathematical property to estimate lcoation, e.g. Trilateration Graph embedding

methods

L3

N

L1 L2

6

N L1 L2 L3

N 0 r1 r2 r3

L1 r1 0 d1 d3

L2 r2 d1 0 d2

L3 r3 d3 d2 0L2

L3

L1

Nr1

r3

r2d3 d2

d1

embedding methods

trilateration

Pros and Cons

7

Pros Simple No expensive hardware

Often requires clock synchronization to calculate distances Accuracy is prone to signal fluctuation and clock

synchronization

Angle-based Techniques

Measure angles between the node and landmarks

Use mathematical properties to estimate location, e.g. 2-angle triangulation

(angles measured at landmarks)

3-angle triangulation (angles measured at mobile node)

8

2-angle triangulation3-angle triangulation

trilateration

Pros and Cons

9

Pros Less sensitive to signal attenuation Simple calculation (transformable into

trilateration) Cons

Requires special hardware (directional antennas) to measure angles

Can be affected by reflections or multipaths

Pattern-based Techniques

Associate observed patterns with location

Training Phase Measure signal

patterns at reference points

Establish a mapping between them

Online Phase Observe pattern at

unknown position Compare with trained

data Estimate location10

BS3

BS1

BS2

Pros and Cons

11

Pros Fast estimation (just a look up) Accurate (if the map is current)

Cons Time-consuming and labor-intensive training

phase Map has to be current; not adaptive to

environmental changes

Electro-Magnetic Tag Approach Technologies

Infrared (IR) tags Ultrasonic Radio Frequency Identification (RFID) UWB (Ultra-wide band) Etc.

Characteristics Higher accuracy due to shorter range Some require line-of-sight

This category of techniques may be part of a localization system and provides alternative references to improve accuracy

12

Comparison of Tag SchemesScheme Technique Components Examples Pros Cons

RFID RSS or direct referencing with RFID tag positions

Active/passive RFID tags

1. SpotON 2. LANDMARC

1. Low cost 2. Easy to deploy 3. Insensitive to NLOS

1. High density 2. Require reader with intense signal output

Bluetooth

RSS from ISM band

Positioning server, Wireless AP, Bluetooth tags

Topaz 1. High accuracy Short distance (and hence high density)

Ultrasound

RSS + RTOF (Roundtrip Time of Flight)

Ultrasound transceiver

1. Cricket 2. SmartLOCUS

1. Improved accuracy on time measurement because of lower transmission speed than EM wave 2. Low cost

Sensitive to the shapes of surface and the density of the material

Ultra Wide Bandwidth (UWB)

Transmitting signal over multiple bands of frequency simultaneously

UWB tags 1. Ubisense system

2. Sappire Dart

1. Can be used close to other RF signals without interference 2. Low power consumption 3. Signals are easy to detect and filter

High system cost due to relatively new technology

Inertial Navigation System (INS) Key components

Motion sensor Rotation sensor Acceleration sensor Etc.

Characteristics Continuously compute location based on previous location and

sensor information No external references needed Accumulation of errors over time

Performance of INS largely depends on drift compensation scheme in order to reduce propagation error

Integral computation Computationally intensive and error-prone

14

Factors of Inaccuracies

15

Not all techniques are 100% accurate Signal fading or transient signal fluctuation Measurement noise or uncertainty Clock synchronization or inaccuracy Landmark density Accumulation of errors over time (for INS) Environmental changes (for

pattern-based/fingerprinting) Lack of updates or measurement granularity

Etc.

How to Achieve Higher Accuracy?

16

Augment upon the existing infrastructure Providing a natural transition path toward higher

accuracy Cost-effective

No expensive hardware For populated areas

Cooperative (Peer-to-peer) estimation Mobiles help each other to achieve better

accuracy Multiple estimations

Estimation techniques do not have to be treated in isolation

Combining their estimations for better accuracy

Collaborative Localization Using Peer-to-Peer Technique

17

Infrastructure and Mobile Noes

18

Infrastructure Some landmarks or access points (APs) to provide

basic localization Due to deployment cost, the accuracy is not high

Mobile nodes Limited computational power, transmission range

and battery life High density over the infrastructure network Form a mobile ad-hoc network to better estimate

their locations Achieving better localization using

cooperative mobile nodes

The Localization Scheme: Local Estimation (1)

Construct a table of neighbors by varying a node’s transmission range Quantized Distance

Vector (QDV) construction

20

1

2

3

4

52 15 24 3

QDV

Identifier

Distance Level

Location Estimation (2)

mISOMAP1. Collect QDVs from

neighbors2. Compile QDVs

locally3. Generate

embedding using Multi-dimensional Scaling (MDS)

21 2

65

4

31

MDS

Infrastructure to Fix the Embedding Embedding transformation

Requires at least 3 references to “fix” the embedding

23

2

65

4

312

65

4

3 1

2

65

4

31

2

6

5

43

1

Reflection

Rotation Translation

Localization Spreads Like a Ripple from Landmarks

Starts with a landmark doing the local estimation, then spreads to its neighboring nodes

Nodes receiving location updates become references of others

25

Combining Estimations from Different Landmarks Together

Map refinement Combines

several relative positions to generate an absolute position by minimizing:

26

px : absolute positionpLi : relative position to BN idxLi : distance from BN i

Locations are Well Estimated

32

Real positions Estimated positionsNormalized Average actual distance error = 0.2805

Normalized Average relative distance error = 0.24883

Summary

36

A collaborative localization scheme Distance-based Improves the accuracy of infrastructure network

Only requires quantized distance measurement Robust to measurement noise

Only requires signal power control No special hardware requirement No global synchronization

Only involves neighbor communication Low power consumption

Fully distributed Supports network dynamics

Estimation Fusion

40

Research Motivation Many indoor location techniques deployed

Wi-Fi, RFID, GPS, INS, etc. Locations are estimated in isolation Different level of errors

Due to measurement noise, base-station density, calibration accuracy, etc.

A handheld may have all these estimations at the same time

41

Objective: Combine, or fuse, estimations to attain better localization accuracy

1. Characterization of estimation errors of different localization techniques

Angle of arrival (AOA) Time different of arrival (TDOA) Roundtrip time of flight (RTOF) Inertial Navigation System (INS)

2. Given errors, optimally combine them With efficient, simple and distributed algorithm With environmental or topological constraints

42

Localization Error: Angle of Arrival (AOA) AOA: angle between BS and MS

: AOA : coordinates of base station i : coordinates of the mobile : measurement noise

),( ii yx

),( yx

),0(~ 2 N

43

Estimation Error Variance of the estimation

Related to 2 factors Distance between mobile and BS Variance of measurement noise

44

Close Match Between Simulation and Analysis

Number of BS = 6

45

Estimation Error Decreases with Base Stations

= 10 degrees

46

Estimation Error: Time Difference of Arrival (TDOA)

TDOA1. Get time difference from the mobile

to different base stations2. Draw hyperbola for every set of time

difference3. Obtain the intersection point as the

mobile location Time Difference:

: : : Distance

measurement noise :

Synchronization noise

1TTi 1DDi

),0(~ 2 Np

),(~ fcfcTric

47

Estimation Error Decreases with Base Stations

49

Roundtrip Time of Flight (RTOF) TDOA requires synchronization of clocks between base

stations and devices TROF does not require that System components:

Clock Base stations

• Noise assumed: Clock shift: )1,0(~U

Round Trip Time of Flight

RTT: Round Trip Time

Estimation Error

Error Analysis of Inertial Navigation System System components:

Gyroscope : measure orientation Accelerometer: measure acceleration

• Two noise assumed: Gyroscope: Accelerometer:

a

),0(~ 21 N

22,0 N

Error Analysis

Possible Estimation

Possible Estimation

Distance Error

Degree Error

Estimation Error w.r.t. the Accelerometer Error

Super-linear Increase in Estimation Error with the Duration of Using INS

Estimation Error is Similar to a Normal

Estimation Fusion Given a number of estimations with location uncertainties, how

to optimally combine them?

Estimation i: Xi ~ N( xi , sigmai), Yi ~ N( yi , sigmai) Find a coordinate minimizing the expected distances to all

these estimations67

Problem Formulation Objective function

68

Optimal Solution Optimal solution is a point estimate:

Closed-form expressions Simple and efficient computation

69

Estimator Error Decreases with the Number of Estimators

71

Topological Constraint: Alley

73

Topological Constraint: Wall

74

Topological Constraint: Corner

75

Topological Constraint: Room

76

Conclusion Good indoor LBS depends on accurate estimation of

mobile location Many indoor techniques have been studied and

deployed Studied in isolation

Cooperative localization Augment on top of existing infrastructure Mobile peers exchange messages with each other to attain

better accuracy than infrastructure alone Estimation fusion

Optimally combining multiple estimations to attain better accuracy

Estimation uncertainty characterization Simple closed-form solutions to combine them

79

Thank YouQ&A

80