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Sensors 2016, 16(9), 1454; doi:10.3390/s16091454

Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications

School of STEM, Division of Engineering and Mathematics, University of Washington Bothell, Bothell, WA 98011, USA
Academic Editors: Mianxiong Dong, Zhi Liu, Anfeng Liu and Didier El Baz
Received: 28 April 2016 / Revised: 2 September 2016 / Accepted: 2 September 2016 / Published: 9 September 2016
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
View Full-Text   |   Download PDF [309 KB, uploaded 9 September 2016]   |  

Abstract

For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important consideration in developing a distributed particle filtering algorithm in wireless sensor networks is reducing the size of data exchanged among the sensors because of power and bandwidth constraints. In this paper, we propose a distributed particle filtering algorithm with the objective of reducing the overhead data that is communicated among the sensors. In our algorithm, the sensors exchange information to collaboratively compute the global likelihood function that encompasses the contribution of the measurements towards building the global posterior density of the unknown location parameters. Each sensor, using its own measurement, computes its local likelihood function and approximates it using a Gaussian function. The sensors then propagate only the mean and the covariance of their approximated likelihood functions to other sensors, reducing the communication overhead. The global likelihood function is computed collaboratively from the parameters of the local likelihood functions using an average consensus filter or a forward-backward propagation information exchange strategy. View Full-Text
Keywords: particle filtering; target-tracking; sensor networks; consensus filter; forward-backward particle filtering; target-tracking; sensor networks; consensus filter; forward-backward
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ghirmai, T. Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications. Sensors 2016, 16, 1454.

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