Target tracking is investigated using particle filtering of data collected by distributed sensors. In lieu of a fusion center, local measurements must be disseminated across the network for each sensor to implement a centralized particle filter (PF). However, disseminating raw measurements incurs formidable communication overhead as large volumes of data are collected by the sensors. To reduce this overhead and thus enable distributed PF implementation, the present paper develops a set-membership constrained (SMC) PF approach that i) exhibits performance comparable to the centralized PF; ii) requires only communication of particle weights among neighboring sensors; and iii) can afford both consensus-based and incremental averaging implementations. These attractive attributes are effected through a novel adaptation scheme, which is amenable to simple distributed implementation using min- and max-consensus iterations. The resultant SMC-PF exhibits high gain over the bootstrap PF when the likelihood is peaky, but not in the tail of the prior. Simulations corroborate that for a fixed number of particles, and subject to peaky likelihood conditions, SMC-PF outperforms the bootstrap PF, as well as recently developed distributed PF algorithms, by a wide margin.
Bibliographical noteFunding Information:
Manuscript received July 19, 2010; revised February 09, 2011 and May 17, 2011; accepted June 01, 2011. Date of publication June 16, 2011; date of current version August 10, 2011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Andrea Cavallaro. Work in this paper was supported through collaborative participation in the C&N Consortium sponsored by the U. S. ARL under the CTA Program, Cooperative Agreement DAAD19-01-2-0011. The U. S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. Support was also provided by the National Science Foundation (IIS-0643680).
- particle filter
- sensor network