Enhancing Positioning Accuracy Through RSS Based Ranging and Weighted Least Square

Approximation

How to enhance RSS based ranging and localization by learning the channel ?

Mohamed LaaraiedhStéphane Avrillon

Bernard Uguen

POCA 09 - AntwerpenMay 28, 2009

IETR Labshttp://www.ietr.org

University of Rennes 1

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Context and Motivations

• RSS measurements are less accurate then time based observables (ToA,TDoA)

• RSS is usually available for free

• RSS can be modelled as a function of distance: Path Loss models

• Path loss models can be updated using RSS measurements

• How can the knowledge of channel enhance RSS based localization and ranging accuracies?

1/12

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Outline

Learning of radio propagation channel

RSS based ranging estimators

RSS based Localization

Simulations and Results

Conclusions and Perspectives

2/12

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Ranging and Localization

3/12

Ranging Step

RSS1 RSS2 RSSn…

r1 r2 rn…

Range Based Estimator

Position x

WLSLS

Localization Step

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Indirect estimators: RSS ranging

2SmedianMLd d e

media

Mnd e

Me

2(0, )shX N 0 100

log (10 )d

L L n Xd

ln1

0

0

1shSn

00

ln10ln

10

L LM

nd

2

2

2

ln

2

1( , )

d M

dS

Sdp d L e

2M Se 2

2

SMe

d

4/12

2

2mean

SM

d e

2 22 2 3 ( 1)M S Smean e e

2 22 2 ( 1)M S SMedian e e

2 22 2 2 (1 )M S SML e e

2 2 2mML edian mean

To get more sophisticated estimators of position, variances must be considered.

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Learning of Radio Channel

It is necessary to learn the Path Loss Model Parameters from the channel.

10log d

( )L dBHow to improve Path Loss Modelrelevance ?

For each fixed AP or BS

Continuously update and keep track of 3 numbers

0 0 1, , ( , , , , , )ksh k shk kn L LR k L d n L

5/12

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

LS and WLS Approximations

6/12

11( )2

T Tx A A A h 1 1 11

( )2

T T x A C A A C h

2 1 2 1

1 1

... ...

K K

x x y y

x x y y

A

2 2 2 2 2 22 1 2 1 1 2

2 2 2 2 2 21 1

ˆ ˆ

...

ˆ ˆK K K K

x x y y d d

x x y y d d

h

2

2..ˆdiag k

k K

C

evaluated from K anchor nodes positions

evaluated from estimated ranges and anchor nodes coordinates

LS estimator WLS estimator

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Simulations and Results

7/12

Path loss Parameters

Indoor Outdoor

np1.6 to 1.82 to 4

l(m)0.12 0.33

σsh2 to 52 to 5

Square Length (m)

151000

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Simulations and Results

8/12

Performances in outdoor scenario

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Simulations and Results

9/12

Performances in indoor scenario

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Conclusions & Perspectives

A new ML estimator of Ranges from RSS observables is proposed.

Localization and Ranging accuracies depend on PL parameters.

Evaluate these estimators on Real Measurements and Ray tracing simulations.

10/12

How interesting is the learning of channel for localization and ranging.

Localization accuracy depends on the used technique for RSS ranging.

Pipe these estimators in Tracking processes using Klaman and Particle Filters.

A direct approach for RSS based localization is already published in VTC Spring

Mohamed Laaraiedh, POCA 2009 – Antwerpen – May 28, 2009

Bibliography

11/12

[1] P. Bellavista, A. Kupper, and S. Helal, “Location-based services: Back to the future,” IEEE, Pervasive Computing, 2008.[2] “http://www.kn-s.dlr.de/where/.”[3] H. Laitinen, S. Juurakko, T. Lahti, R. Korhonen, and J. Lahteenmaki, “Experimental evaluation of location methods based on signal-strength measurements,” IEEE transactions on vehicular technology, vol. 56, Jan. 2007.[4] A. Goldsmith, Wireless communications. 2005.[5] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on systems, man, and cybernetics, vol. 37, Nov. 2007.[6] K. Cheung, H. So, W. Ma, and Y. Chan, “A constrained least squares approach to mobile positioning: Algorithms and optimality,” 2006.[7] T. Gigl, G. J. M. Janssen, V. Dizdarevic, K. Witrisal, and Z. Irahhauten, “Analysis of a uwb indoor positioning system based on received signal strength,” WPNC 07, 2007.[8] M. Sugano and T. Kawazoe, “Indoor localization system using rssi measurement of wireless sensor network based on zigbee standard,” WSN 06, July 2006.[9] S. Frattasi, M. Monti, and P. Ramjee, “A cooperative localization scheme for 4g wireless communications,” IEEE Radio and Wireless Symposium, 2006.[10] V. Abhayawardhana, W. Crosby, M. Sellars, and M. Brown, “Comparison of empirical propagation path loss models for fixed wireless access systems,” IEEE VTC spring, 2005.[11] K. Whitehouse, C. Karlof, and D. Culler, “A practical evaluation of radio signal strength for ranging-based localization,” Mobile Computing and Communications Review, vol. 11, no. 1, 2007.[12] M. P.McLaughlin, A Compendium of Common Probability Distributions, vol. Regress+ Documentation. 1999.[13] M.Laaraiedh, S.Avrillon, B.Uguen. Hybrid Data Fusion Techniques for Localization in UWB Networks. In Proceedings WPNC Hanover, Germany, March 2009.[14] S. Sand, C. Mensing, M. Laaraiedh, B. Uguen, B. Denis, S. Mayrargue, M. García, J. Casajús, D. Slock, T. Pedersen, X. Yin, G. Steinboeck, and B. H. Fleury. Performance Assessment of Hybrid Data Fusion and Tracking Algorithms. In Accepted for publication in Proceedings ICT Mobile Summit (ICT Summit 2009), Santander, Spain, June 2009.[15] M.Laaraiedh, S.Avrillon, B.Uguen. Enhancing positioning accuracy through RSS based ranging and weighted least square approximation. POCA, Antwerp, Belgium, May, 2009.