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



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.



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Correspondence to Zhi Zhang.

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Zhang, Z., Li, J. & Liu, L. Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation. Sci. China Inf. Sci. 59, 1–15 (2016). https://doi.org/10.1007/s11432-015-5415-6

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  • 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