This paper investigates the distributed state estimation for a class of linear time-varying systems with intermittent observations in sensor networks. Unlike the existing studies in distributed state estimation, this work considers the scenario where the cross-covariances between different sensors are unavailable and the measurements for state estimation encounter intermittent observations and/or random losses. For this practical scenario, a new sequential covariance intersection-based Kalman consensus filer (SCIKCF) is then developed. We show that, with the proposed SCIKCF, each sensor can achieve consensus estimates regardless of the order of fusion. Furthermore, the stability of the SCIKCF as well as the boundedness of the estimation error and the corresponding error covariances are analysed. Finally, three examples are performed to verify the effectiveness of the proposed SCIKCF.

中文翻译：

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间歇观测的基于序列协方差交点的卡尔曼共识滤波器

本文研究了传感器网络中具有间歇观测的一类线性时变系统的分布状态估计。与现有的分布式状态估计研究不同，这项工作考虑了以下情况：不同传感器之间的互协方差不可用，状态估计的测量会遇到间歇性观察和/或随机损失。对于此实际情况，然后开发了一个新的基于顺序协方差交点的卡尔曼共识文件（SCIKCF）。我们证明，通过提出的SCIKCF，无论融合顺序如何，每个传感器都可以实现共识估计。此外，分析了SCIKCF的稳定性以及估计误差的有界性和相应的误差协方差。最后，