NDLI: Distributed lightweight target tracking for wireless sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Sheng Wang Xue Wang Yong Wang Xinyao Sun
Copyright Year	2009
Description	Author affiliation: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China (Sheng Wang; Xue Wang; Yong Wang; Xinyao Sun)
Abstract	Target tracking is an important task addressed in wireless sensor network (WSN), because the resources of WSN are limited. A distributed lightweight particle filter algorithm is proposed to achieve robust target tracking with low resource requirement in the WSN consisting of acoustic sensor nodes. In the proposed algorithm, each sensor node carries out partial particle filter with its local data and the neighbor nodes' data in distributed manner. Then local results of selected sensor nodes are fused to make final decision. To simplify the computation of particle filter, lightweight sampling and resampling schemes are introduced, where a simple range-free algorithm is adopted to restrict potential area of target's location. The experimental results verify that the proposed distributed lightweight particle filter algorithm can effectively achieve target tracking in WSN with low resource consumption. Compared to previous target localization algorithms, such as maximum likelihood estimation and centralized particle filter, the proposed algorithm has outstanding performance in accurate target tracking and low resources consumption.
Starting Page	870
Ending Page	875
File Size	1823961
Page Count	6
File Format	PDF
ISBN	9781424451135
DOI	10.1109/MOBHOC.2009.5337044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wireless sensor networks Target tracking Instruments Acoustic sensors Sensor fusion Sampling methods Particle filters Hardware Robustness State estimation
Content Type	Text
Resource Type	Article

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