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Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering

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Abstract

A novel networked data-fusion method is developed for the target tracking in wireless sensor networks (WSNs). Specifically, this paper investigates data fusion scheme under the communication constraint between the fusion center and each sensor. Such a message constraint is motivated by the bandwidth limitation of the communication links, fusion center, and by the limited power budget of local sensors. In the proposed scheme, each sensor collects one noise-corrupted sample, performs a quantizing operation, and transmits quantized message to the fusion center. Then the fusion center combines the received quantized messages to produce a final estimate. The novel data-fusion method is based on the quantized measurement innovations and decentralized Kalman filtering (DKF) with feedback. For the proposed algorithm, the performance analysis of the estimation precision is provided. Finally, Monte Carlo simulations show the effectiveness of the proposed scheme.

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Authors and Affiliations

  1. Department of Automation, Science and Technology on Avionics Integration Laboratory, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jian Xu & JianXun Li

  2. Department of Automation, System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jian Xu & JianXun Li

  3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Sheng Xu

Authors
  1. Jian Xu
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  2. JianXun Li
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  3. Sheng Xu
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Correspondence to Jian Xu.

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Xu, J., Li, J. & Xu, S. Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering. Sci. China Inf. Sci. 55, 530–544 (2012). https://doi.org/10.1007/s11432-011-4533-z

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  • DOI: https://doi.org/10.1007/s11432-011-4533-z

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