Abstract This paper is concerned with the distributed recursive filtering for the discrete-time nonlinear multi-sensor networked system with multi-step sensor delays, missing measurements and correlated noise. Based on the innovation statistical distance, an adaptive time delay estimation method, which belongs to the online methods, is derived to determine whether the measurement is acquired or not along with the time delay step. Then, a nonlinear system model is founded based on a set of selected Bernoulli distributed random variables to describe the multi-step sensor delays, missing measurements and correlated noise. The obtained time delay step can be used to update parameters of the proposed measurement model. Next, a distributed recursive filtering is designed based on linear fitting (LF) and weighted average consensus (WAC) to solve the nonlinear state estimation in the multi-sensor networked system. Meanwhile, a selection strategy is designed based on the innovation statistical distance for the weighted factors to improve the distributed fusion accuracy. Further, filtering errors of the distributed recursive filtering are proved to be exponentially bounded in mean square. Numerical simulations are conducted to evaluate the performance of the proposed algorithm.

中文翻译：

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具有多步传感器延迟、丢失测量值和相关噪声的多传感器网络系统的分布式递归滤波

摘要 本文研究了具有多步传感器延迟、缺失测量和相关噪声的离散时间非线性多传感器网络系统的分布式递归滤波。基于创新统计距离，推导出一种自适应时延估计方法，属于在线方法，用于确定是否与时延步长一起获取测量。然后，基于一组选定的伯努利分布随机变量建立非线性系统模型来描述多步传感器延迟、缺失测量和相关噪声。获得的时间延迟步长可用于更新所提出的测量模型的参数。下一个，基于线性拟合(LF)和加权平均一致性(WAC)设计分布式递归滤波解决多传感器网络系统中的非线性状态估计问题。同时，设计了一种基于加权因子的创新统计距离的选择策略，以提高分布式融合的准确性。此外，分布式递归滤波的滤波误差被证明在均方中呈指数有界。进行数值模拟以评估所提出算法的性能。证明分布式递归滤波的滤波误差在均方中呈指数有界。进行数值模拟以评估所提出算法的性能。证明分布式递归滤波的滤波误差在均方中呈指数有界。进行数值模拟以评估所提出算法的性能。