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Recursive Filtering for Time-Varying Systems With Random Access Protocol

机译:具有随机访问协议的时变系统的递归滤波

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This paper is concerned with the recursive filtering problem for a class of networked linear time-varying systems subject to the scheduling of the random access protocol (RAP). The communication between the sensor nodes and the remote filter is implemented via a shared network. For the purpose of preventing the data from collisions, only one sensor node is allowed to get access to the network at each time instant. The transmission order of sensor nodes is orchestrated by the RAP scheduling, under which the selected nodes obtaining access to the network could be characterized by a sequence of independent and identically-distributed variables. The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant. The desired filter gain is calculated recursively by solving two Riccati-like difference equations. Furthermore, the boundedness issue of the corresponding filtering error covariance is investigated. Sufficient conditions are obtained to ensure the lower and upper bounds of the filtering error covariance. Two illustrative examples are given to demonstrate the correctness and effectiveness ofour developed recursive filtering approach.
机译:本文涉及一类受随机访问协议(RAP)调度的网络线性时变系统的递归过滤问题。传感器节点和远程过滤器之间的通信是通过共享网络实现的。为了防止数据冲突,每个时刻仅允许一个传感器节点访问网络。传感器节点的传输顺序通过RAP调度进行协调,在该调度下,可以通过一系列独立且分布均匀的变量来表征获得对网络的访问的所选节点。解决的滤波问题的目的是设计一种递归滤波器,从而通过适当地设计每个时刻的滤波器增益可以使滤波误差协方差最小化。通过求解两个类似Riccati的差分方程,递归计算出所需的滤波器增益。此外,研究了相应的滤波误差协方差的有界性问题。获得足够的条件以确保滤波误差协方差的下限和上限。给出两个说明性的例子,以证明我们开发的递归过滤方法的正确性和有效性。

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