This paper is concerned with the distributed state estimation problem over wireless sensor networks. The communication links are unreliable that are subject to random link failures modeled as a set of independent Bernoulli processes. To estimate the plant state collaboratively, a Kalman-consensus filtering approach is developed where the sensors spread the local information obtained from the Kalman filtering algorithm by performing a consensus of the inverse covariance matrices at each time instant. Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established. It is shown that the filtering performance is directly influenced by the network connectivity and the collective observability. A numerical example is illustrated to verify the proposed results.

中文翻译：

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传感器网络上随机链路故障的卡尔曼一致性过滤


本文关注无线传感器网络的分布式状态估计问题。通信链路不可靠，会受到建模为一组独立伯努利过程的随机链路故障的影响。为了协作估计植物状态，开发了卡尔曼一致性滤波方法，其中传感器通过在每个时刻执行逆协方差矩阵的一致性来传播从卡尔曼滤波算法获得的局部信息。建立了卡尔曼一致性滤波器的随机有界性的充分条件。结果表明，过滤性能直接受到网络连通性和集体可观测性的影响。一个数值例子来验证所提出的结果。