This article considers a distributed Kalman filtering problem for linear system contaminated by complex multi-channel random uncertain parameter in which a number of nodes cooperative without central coordination to estimate a common state based on local measurements and data received from neighbors. We propose an approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. Then, we extend this method to smoothing and deconvolution algorithm. Finally, simulation experiments demonstrate the validity of the proposed approach.

中文翻译：

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具有复杂多通道随机不确定参数和解耦局部滤波器的线性系统分布式卡尔曼滤波器

本文考虑了受复杂多通道随机不确定参数污染的线性系统的分布式卡尔曼滤波问题，其中多个节点在没有中央协调的情况下进行协作，以根据本地测量值和从邻居接收到的数据来估计共同状态。我们提出了一种消除这种错误传播的方法。所提出的局部滤波器保证在某些全局结构数据的某些温和条件下是稳定的，并且它们的融合产生集中的卡尔曼估计。然后，我们将此方法扩展到平滑和反卷积算法。最后，仿真实验证明了所提出方法的有效性。