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

science china(Information sciences) 2012年第3期55卷 530-544页

作者： xu jian1,2.,{2.,{2., science and technology on avionics integration laboratory, shanghai jiao tong university, shanghai 2.02.0, china 2.7} of automation, System Control and Information Processing, Ministry of Education of china, shanghai jiao tong university, shanghai 2.02.0, china 3School of Mechanical Engineering, shanghai jiao tong university, shanghai 2.02.0, china 1. department of automation Science and Technology on Avionics Integration Laboratory Shanghai Jiao Tong University Shanghai 200240 China2. Department of Automation System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai 200240 China3. School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China

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.

关键词： data fusion target tracking limited bandwidth quantized innovation Kalman filtering

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Quantized innovations Kalman filter: stability and modification with scaling quantization

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Journal of Zhejiang university-{{2. C(Computers and Electronics) 2012年第2期13卷 118-130页

作者： jian xu jian-xun li {2. xu science and technology on {{2. integration laboratory Shanghai Jiao Tong UniversityShanghai 200240 MOE Key laboratory of System Control and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms.

关键词： Kalman filtering Quantized innovation Stability Scaling quantization Wireless sensor network

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