NLOS Identification Using a Hybrid ToA-Signal Strength Algorithm for Underwater Acoustic Localizatio...
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NLOS Identification Using a Hybrid ToA-Signal Strength Algorithm for Underwater Acoustic Localization. *. By : Roee Diamant, Hwee-Pink Tan and Lutz Lampe University of British Columbia (UBC), Institute of InfoComm Research. *. 1. Outline.

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By roee diamant hwee pink tan and lutz lampe

NLOS Identification Using a Hybrid ToA-Signal Strength Algorithm for Underwater Acoustic Localization

*

By : Roee Diamant, Hwee-Pink Tan and Lutz Lampe

University of British Columbia (UBC), Institute of InfoComm Research

*

1


By roee diamant hwee pink tan and lutz lampe

Outline

  • The problem of NLOS identification in underwater acoustic localization

  • Channel Model and basic assumptions

  • An algorithm for obstacle NLOS classification

  • Sea trial results

2


By roee diamant hwee pink tan and lutz lampe

The Problem of NLOS in Localization

  • Underwater acoustic attenuation models are hard to find -> Localization is mostly based on ToA distance estimation

  • Most existing underwater acoustic localization schemes, e.g., [1-5], implicitly assume that localization messages are received based on line-of-sight (LOS) acoustic links

  • Therefore, localization algorithms only consider ToA measurement noise (affected by e.g., time-synchronization, multipath, nodes motions)

  • However, obstacles in the channel may cause nonline-of-sight (NLOS) scenarios in which only echoes of the transmitted signal arrive at the receiver (Obstacle NLOS)

If not identified, Obstacle NLOS link considerably reduces localization accuracy

3


System model and assumptions

System Model

System Model and Assumptions

Obstacle NLOS

NLOS Classification

Sea trial results

4


System model and assumptions 2

System Model

System Model and Assumptions (2)

Obstacle NLOS

NLOS Classification

Sea trial results

Distance to the reflecting surface

and to the destination

We expect considerable difference between ToA and signal strength

distance estimations in an Obstacle NLOS link

5


Nlos classification

System Model

NLOS Classification

Obstacle NLOS

NLOS Classification

Sea trial results

Efficacy of the algorithm relies on the validity of the assumption that the TS+SL component is much larger than the effects of measurement noise or attenuation model inaccuracies.

6


Performance analysis

System Model

Performance Analysis

Obstacle NLOS

NLOS Classification

Sea trial results

“True” distance

Distance measurement

noise variance

7


Simulations

System Model

Simulations

Obstacle NLOS

NLOS Classification

Sea trial results

8


Sea trial description

System Model

Sea Trial Description

Obstacle NLOS

NLOS Classification

Sea trial results

9


Sea trial results

System Model

Sea Trial Results

Obstacle NLOS

NLOS Classification

Sea trial results

All Obstacle NLOS and LOS links were identified correctly

10


By roee diamant hwee pink tan and lutz lampe

Summary

If not detected,

considerably affects localization accuracy

NLOS identification problem

Accurate models are hard to achieve.

We rely only on lower bound on attenuation

Attenuation model

Target strength and spreading loss

lead to a noticeable difference between

ToA and SS distance estimations

ToA vs. signal strength distance estimation

Distance estimations

need not be accurate

Thresholding: compare both distance estimations

Performed in a harbor environment

with several Obstacle NLOS links

Sea trial to validate performance

All Obstacle NLOS and Loss links were identified

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By roee diamant hwee pink tan and lutz lampe

Reference list

  • [1] W. Burdic, “Underwater Acoustic System Analysis,” Los Altos, CA, USA: Peninsula Publishing, 2002

  • [2] X. Cheng, H. Shu, Q. Liang, and D. Du, “Silent Positioning in Underwater Acoustic Sensor Networks,” IEEE Trans. Veh. Technol.,

  • vol. 57,no. 3, pp. 1756–1766, May 2008.

  • [3] W. Cheng, A. Y. Teymorian, L. Ma, X. Cheng, X. Lu, and Z. Lu, “3D Underwater Sensor Network Localization,” IEEE Trans. on Mobile Computing, vol. 8, no. 12, pp. 1610–1621, December 2009.

  • [4] L. Mu, G. Kuo, and N. Tao, “A novel ToA location algorithm using LOS range estimation for NLOS environments,” in

  • Proc. of the IEEE Vehicular Technology Conference (VTC), Melbourne, Australia, May 2006, pp. 594–598.

  • [5] S. Fischer, H. Grubeck, A. Kangas, H. Koorapaty, E. Larsson, and P. Lundqvist, “Time of arrival estimation of

  • narrowband TDMA signal for mobile positioning,” Proc. of the IEEE International Symposium on Personal, Indoor

  • and Mobile Radio Communications (PIMRC), pp. 451–455, September 1998.

  • [6] S. Woo, H. You, and J. Koh, “The NLOS mitigation technique for position loacation using IS-95 CDMA networks,”

  • Proc. of the IEEE Vehicular Technology Conference (VTC), pp. 2556–2560, September 2000.

  • [7] P. C. Chen, “A non-line-of-sight error mitigation algorithm in location estimation,” Proc. of the IEEE Wireless

  • Communications and Networking Conference (WCNC), pp. 316–320, September 1999.

  • [8] L. Cong and W. Zhuang, “Non-line-of-sight error mitigation in TDoA mobile location,” Proc. of the IEEE International

  • Conference on Global Telecommunications (GlobeCom), vol. 1, pp. 680–684, November 2001

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By roee diamant hwee pink tan and lutz lampe

Thank you

Questions?

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