Particle filtering-based target tracking in binary sensor networks using adaptive thresholds

Mahesh Vemula; Mónica F. Bugallo; Petar M. Djurić

doi:10.1109/CAMSAP.2007.4497954

Particle filtering-based target tracking in binary sensor networks using adaptive thresholds

Stony Brook University

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

13 Scopus citations

Abstract

Target tracking in wireless sensor networks with constrained resources is a challenging problem. In this paper we consider scenarios where sensors sense an object of interest and process the received measurements using adaptive thresholds to obtain quantized data in the form of two levels. The data are quantized to address resource constraints in sensor networks. The processed data are then sent to a fusion center which resolves the tracking problem by means of a particle filter which can handle non-linearities in the state model. The performance of various strategies for threshold adaptation is studied by computer simulations and the results reveal that improvement is obtained over scenarios with fixed thresholds.

Original language	English
Title of host publication	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Pages	17-20
Number of pages	4
DOIs	https://doi.org/10.1109/CAMSAP.2007.4497954
State	Published - 2007
Event	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP - St. Thomas, Virgin Islands, U.S. Duration: Dec 12 2007 → Dec 14 2007

Publication series

Name	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Conference

Conference	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Country/Territory	Virgin Islands, U.S.
City	St. Thomas
Period	12/12/07 → 12/14/07

Keywords

Adaptive signal processing
Monte Carlo methods
Target tracking

Access to Document

10.1109/CAMSAP.2007.4497954

Cite this

Vemula, M., Bugallo, M. F., & Djurić, P. M. (2007). Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP (pp. 17-20). Article 4497954 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP). https://doi.org/10.1109/CAMSAP.2007.4497954

Vemula, Mahesh ; Bugallo, Mónica F. ; Djurić, Petar M. / Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. pp. 17-20 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP).

@inproceedings{b37056dd9a2f4b20bebecac6afdbcff5,

title = "Particle filtering-based target tracking in binary sensor networks using adaptive thresholds",

abstract = "Target tracking in wireless sensor networks with constrained resources is a challenging problem. In this paper we consider scenarios where sensors sense an object of interest and process the received measurements using adaptive thresholds to obtain quantized data in the form of two levels. The data are quantized to address resource constraints in sensor networks. The processed data are then sent to a fusion center which resolves the tracking problem by means of a particle filter which can handle non-linearities in the state model. The performance of various strategies for threshold adaptation is studied by computer simulations and the results reveal that improvement is obtained over scenarios with fixed thresholds.",

keywords = "Adaptive signal processing, Monte Carlo methods, Target tracking",

author = "Mahesh Vemula and Bugallo, \{M{\'o}nica F.\} and Djuri{\'c}, \{Petar M.\}",

year = "2007",

doi = "10.1109/CAMSAP.2007.4497954",

language = "English",

isbn = "9781424417148",

series = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP",

pages = "17--20",

booktitle = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP",

note = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP ; Conference date: 12-12-2007 Through 14-12-2007",

}

Vemula, M, Bugallo, MF & Djurić, PM 2007, Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. in 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP., 4497954, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP, pp. 17-20, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP, St. Thomas, Virgin Islands, U.S., 12/12/07. https://doi.org/10.1109/CAMSAP.2007.4497954

Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. / Vemula, Mahesh; Bugallo, Mónica F.; Djurić, Petar M.
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. p. 17-20 4497954 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP).

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

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N2 - Target tracking in wireless sensor networks with constrained resources is a challenging problem. In this paper we consider scenarios where sensors sense an object of interest and process the received measurements using adaptive thresholds to obtain quantized data in the form of two levels. The data are quantized to address resource constraints in sensor networks. The processed data are then sent to a fusion center which resolves the tracking problem by means of a particle filter which can handle non-linearities in the state model. The performance of various strategies for threshold adaptation is studied by computer simulations and the results reveal that improvement is obtained over scenarios with fixed thresholds.

AB - Target tracking in wireless sensor networks with constrained resources is a challenging problem. In this paper we consider scenarios where sensors sense an object of interest and process the received measurements using adaptive thresholds to obtain quantized data in the form of two levels. The data are quantized to address resource constraints in sensor networks. The processed data are then sent to a fusion center which resolves the tracking problem by means of a particle filter which can handle non-linearities in the state model. The performance of various strategies for threshold adaptation is studied by computer simulations and the results reveal that improvement is obtained over scenarios with fixed thresholds.

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KW - Monte Carlo methods

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M3 - Conference contribution

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Vemula M, Bugallo MF , Djurić PM. Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. p. 17-20. 4497954. (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP). doi: 10.1109/CAMSAP.2007.4497954

Particle filtering-based target tracking in binary sensor networks using adaptive thresholds

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