Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

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• 作者：Zhi Zhang ; Jianxun Li ; Liu Liu
• 关键词：data fusion ; distributed state estimation ; target tracking ; Kalman filtering ; quantization ; wireless sensor networks ; 022316 ; 数据融合 ; 分布式状态估计
目标跟踪 ; 卡尔曼滤波 量化 ; 无线传感器网络
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：59
• 期：2
• 页码：1-15
• 全文大小：630 KB
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  9.Msechu E J, Ribeiro A, Roumeliotis S I, et al. Distributed Kalman filtering based on quantized innovation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, 2008. 3293–3296
  10.You K, Xie L, Sun S, et al. Multiple-level quantized innovation Kalman filtering. In: Proceedings of the 17th IFAC World Congress, COEX, 2008. 1420–1425
  11.You K, Xie L, Sun S, et al. Quantized filtering of linear stochastic system. Trans Inst Meas Contr, 2011, 33: 683–689CrossRef
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• 作者单位：Zhi Zhang (1) (2)
  Jianxun Li (1) (2)
  Liu Liu (1)
  
  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
  2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

Low energy consumption and limited power supply are significant factors for wireless sensor networks (WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore, quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement. Keywords data fusion distributed state estimation target tracking Kalman filtering quantization wireless sensor networks