This paper studies the distributed fusion filtering (DFF) issue for a class of nonlinear delayed multi-sensor networked systems (MSNSs) subject to multiple fading measurements (MFMs) under stochastic communication protocol (SCP). The phenomenon of MFMs occurs randomly in the network communication channels and is characterized by a diagonal matrix with certain statistical information. In order to decrease the overload of communication network and save network resources, the SCP that can regulate the information transmission between sensors and estimators is adopted. The primary aim of the tackled problem is to develop the DFF method for nonlinear delayed MSNSs in the presence of MFMs and SCP based on the inverse covariance intersection fusion rule. In addition, the local upper bound (UB) of the filtering error covariance (FEC) is derived and minimized by means of suitably designing the local filter gain. Moreover, the boundedness analysis regarding the local UB is proposed with corresponding theoretical proof. Finally, two simulation examples with comparative illustrations are given to display the usefulness and feasibility of the derived theoretical results.

中文翻译：

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通过随机通信协议进行多衰落测量的多传感器非线性网络系统的分布式融合滤波


本文研究了随机通信协议（SCP）下经受多次衰落测量（MFM）的一类非线性延迟多传感器网络系统（MSNS）的分布式融合滤波（DFF）问题。 MFM现象在网络通信信道中随机发生，其特征是具有一定统计信息的对角矩阵。为了减少通信网络的过载并节省网络资源，采用了能够调节传感器和估计器之间信息传输的SCP。所解决问题的主要目的是在存在 MFM 和 SCP 的情况下，基于逆协方差交集融合规则，开发用于非线性延迟 MSNS 的 DFF 方法。此外，通过适当设计局部滤波器增益，导出并最小化滤波误差协方差（FEC）的局部上限（UB）。此外，还提出了局部UB的有界性分析并给出了相应的理论证明。最后给出了两个仿真算例并进行了对比说明，说明了所推导的理论结果的实用性和可行性。