This paper deals with the distributed state estimation problem for an array of discrete time-varying systems over sensor networks under dynamic event-based transmission scheme (DETS), random parameter matrices (RPMs) and dynamic measurement quantization (DMQ). Different from the existing static event-based transmission scheme with fixed threshold, the employed DETS introduces an auxiliary offset variable in the triggering condition to dynamically regulate the inter-event time. The RPMs are considered in both state and observation equations so as to better reflect the engineering reality. A dynamic quantizer is utilized to account for the phenomenon of incomplete measurements during the data transmission. The aim of the addressed problem is to design a distributed state estimator such that, in the simultaneous presence of the DETS, RPMs and DMQ, an upper bound is guaranteed on the estimation error covariance, and such an upper bound is minimized at each time-step by choosing proper gain matrices. To overcome the difficulties induced by the sparseness of the network topology, a matrix simplification technique is proposed. Moreover, a sufficient condition is provided to ensure that the estimation error is bounded in the mean-square sense. Finally, an illustrative example is presented to demonstrate the theoretical results.

中文翻译：

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传感器网络上随机参数和动态量化下的分布式状态估计：基于事件的动态方法

本文研究了基于动态事件的传输方案（DETS），随机参数矩阵（RPM）和动态测量量化（DMQ）下传感器网络上离散时变系统阵列的分布式状态估计问题。与现有的具有固定阈值的基于静态事件的传输方案不同，所采用的DETS在触发条件下引入辅助偏移变量以动态调节事件间时间。在状态方程和观测方程中都考虑了RPM，以便更好地反映工程实际。动态量化器用于解决数据传输过程中测量不完整的现象。解决的问题的目的是设计一种分布式状态估计器，以便在同时存在DETS，RPM和DMQ的情况下，在估计误差协方差上保证了一个上限，并且通过选择适当的增益矩阵在每个时间步长上将这种上限最小化。为了克服网络拓扑稀疏带来的困难，提出了一种矩阵简化技术。此外，提供了充分的条件以确保估计误差在均方意义上有界。最后，给出一个说明性的例子来证明理论结果。提供足够的条件以确保估计误差在均方意义上有界。最后，给出一个说明性的例子来证明理论结果。提供足够的条件以确保估计误差在均方意义上有界。最后，给出一个说明性的例子来证明理论结果。