NDLI: Collaborative target tracking in wireless sensor networks using quantized innovations and Sigma-Point Kalman Filtering

Content Provider	IEEE Xplore Digital Library
Author	Yan Zhou Jianxun Li Dongli Wang
Copyright Year	2009
Description	Author affiliation: Glorious Sun School of Business & Management, Donghua University, Shanghai, China, 200051 (Dongli Wang) \|\| Department of Automation, Shanghai Jiao Tong University, China, 200240 (Yan Zhou; Jianxun Li)
Abstract	The decentralized collaborative target tracking problem in wireless sensor network (WSN) is investigated in the fusion of quantized innovations perspective. A hierarchical fusion structure with feedback from the fusion center (FC) to each deployed sensor is proposed for tracking a target with nonlinear Gaussian dynamics. Probabilistic quantization strategy is employed in the local sensor node to quantize the innovation. After the FC received the quantized innovations, it estimates the state of the target using the Sigma-Point Kalman Filtering (SPKF). To attack the energy/power source and communication bandwidth constraints, we consider the tradeoff between the communication energy and the global tracking accuracy. A closed-form solution to the optimization problem for bandwidth scheduling is given, where the total energy consumption measure is minimized subject to a constraint on the covariance of the quantization noises. Simulation results illustrate that the proposed scheme obtains average percentage of communication energy saving up to 40.8% compared with the uniform quantization, while keeping tracking accuracy very closely to the clairvoyant SPKF even when the latter relies on analog-amplitude measurements.
Starting Page	942
Ending Page	947
File Size	1062548
Page Count	6
File Format	PDF
ISBN	9781424443475
DOI	10.1109/ISIE.2009.5222156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-05
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Collaboration Target tracking Wireless sensor networks Technological innovation Kalman filters Quantization Filtering Bandwidth Energy measurement Feedback
Content Type	Text
Resource Type	Article

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