Target tracking with mobile sensors using cost-reference particle filtering

Yao Li; Petar M. Djurić

doi:10.1109/ICASSP.2008.4518168

Target tracking with mobile sensors using cost-reference particle filtering

Yao Li
, Petar M. Djurić

Electrical Engineering

Stony Brook University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Sequential Monte Carlo (SMC) methods, also referred to as particle filters, have been successfully applied to a variety of highly nonlinear problems such as target tracking with sensor networks. In this paper, we propose the application of a new class of SMC methods named cost-reference particle filters (CRPFs) to target tracking with mobile sensors. CRPF techniques have been shown to be a flexible and robust alternative when there is no knowledge about the probability distributions of the noise in the system. The sensors positioning during tracking is determined by the predicted target's location as obtained by the CRPF. The performance of the method is investigated by simulations and compared to tracking with standard particle filters (SPFs).

Original language	English
Title of host publication	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages	2549-2552
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2008.4518168
State	Published - 2008
Event	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States Duration: Mar 31 2008 → Apr 4 2008

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Conference

Conference	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/Territory	United States
City	Las Vegas, NV
Period	03/31/08 → 04/4/08

Keywords

Cost-reference particle filters
Mobile sensor network
Sequential Monte Carlo methods

Access to Document

10.1109/ICASSP.2008.4518168

Cite this

Li, Y., & Djurić, P. M. (2008). Target tracking with mobile sensors using cost-reference particle filtering. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 2549-2552). Article 4518168 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2008.4518168

@inproceedings{5fcc306d04a74179896dcc40683ca9aa,

title = "Target tracking with mobile sensors using cost-reference particle filtering",

abstract = "Sequential Monte Carlo (SMC) methods, also referred to as particle filters, have been successfully applied to a variety of highly nonlinear problems such as target tracking with sensor networks. In this paper, we propose the application of a new class of SMC methods named cost-reference particle filters (CRPFs) to target tracking with mobile sensors. CRPF techniques have been shown to be a flexible and robust alternative when there is no knowledge about the probability distributions of the noise in the system. The sensors positioning during tracking is determined by the predicted target's location as obtained by the CRPF. The performance of the method is investigated by simulations and compared to tracking with standard particle filters (SPFs).",

keywords = "Cost-reference particle filters, Mobile sensor network, Sequential Monte Carlo methods",

author = "Yao Li and Djuri{\'c}, \{Petar M.\}",

year = "2008",

doi = "10.1109/ICASSP.2008.4518168",

language = "English",

isbn = "1424414849",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "2549--2552",

booktitle = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP",

note = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ; Conference date: 31-03-2008 Through 04-04-2008",

}

Li, Y & Djurić, PM 2008, Target tracking with mobile sensors using cost-reference particle filtering. in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP., 4518168, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 2549-2552, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Las Vegas, NV, United States, 03/31/08. https://doi.org/10.1109/ICASSP.2008.4518168

Target tracking with mobile sensors using cost-reference particle filtering. / Li, Yao; Djurić, Petar M.
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2008. p. 2549-2552 4518168 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Target tracking with mobile sensors using cost-reference particle filtering

AU - Li, Yao

AU - Djurić, Petar M.

PY - 2008

Y1 - 2008

N2 - Sequential Monte Carlo (SMC) methods, also referred to as particle filters, have been successfully applied to a variety of highly nonlinear problems such as target tracking with sensor networks. In this paper, we propose the application of a new class of SMC methods named cost-reference particle filters (CRPFs) to target tracking with mobile sensors. CRPF techniques have been shown to be a flexible and robust alternative when there is no knowledge about the probability distributions of the noise in the system. The sensors positioning during tracking is determined by the predicted target's location as obtained by the CRPF. The performance of the method is investigated by simulations and compared to tracking with standard particle filters (SPFs).

AB - Sequential Monte Carlo (SMC) methods, also referred to as particle filters, have been successfully applied to a variety of highly nonlinear problems such as target tracking with sensor networks. In this paper, we propose the application of a new class of SMC methods named cost-reference particle filters (CRPFs) to target tracking with mobile sensors. CRPF techniques have been shown to be a flexible and robust alternative when there is no knowledge about the probability distributions of the noise in the system. The sensors positioning during tracking is determined by the predicted target's location as obtained by the CRPF. The performance of the method is investigated by simulations and compared to tracking with standard particle filters (SPFs).

KW - Cost-reference particle filters

KW - Mobile sensor network

KW - Sequential Monte Carlo methods

UR - https://www.scopus.com/pages/publications/51449099589

U2 - 10.1109/ICASSP.2008.4518168

DO - 10.1109/ICASSP.2008.4518168

M3 - Conference contribution

AN - SCOPUS:51449099589

SN - 1424414849

SN - 9781424414840

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 2549

EP - 2552

BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP

T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP

Y2 - 31 March 2008 through 4 April 2008

ER -

Target tracking with mobile sensors using cost-reference particle filtering

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this