Jianxun Li

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Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth. We consider the problem of target tracking in a WSN with quantized measurements in this paper. Attention is focused on the design of measurement quantizer with adaptive thresholds. Based(More)
Based on M-estimate, the problem of robust estimation fusion in decentralized architecture when the sensor noises are contaminated by outliers is considered. A simple robust Kalman filtering (RKF) scheme with weighted matrices of innovation sequences is introduced for local state estimation. Then, to avoid both the inconsistency of the Kalman filter and the(More)
—A scalable Sigma-Point Kalman filter (DSPKF) is proposed for distributed target tracking in a sensor network in this paper. The main idea is to use dynamic consensus strategy to the information form sigma-point Kalman filter (ISPKF) that derived from weighted statistical linearization perspective. Each node estimates the global average information(More)