缺少数据
计算机科学
滤波器(信号处理)
过滤问题
伯努利分布
事件(粒子物理)
非线性系统
伯努利原理
递归滤波器
协方差
控制理论(社会学)
传输(电信)
算法
数学
随机变量
滤波器设计
机器学习
统计
人工智能
根升余弦滤波器
电信
物理
工程类
航空航天工程
量子力学
控制(管理)
计算机视觉
作者
Jun Hu,Zidong Wang,Fuad E. Alsaadi,Tasawar Hayat
标识
DOI:10.1016/j.inffus.2017.03.003
摘要
This paper is concerned with the recursive filtering problem for a class of time-varying nonlinear stochastic systems in the presence of event-triggered transmissions and multiple missing measurements with uncertain missing probabilities. The measurements from different sensors may undergo the missing phenomena, which are characterized by a set of mutually independent Bernoulli random variables and the missing probabilities could be uncertain. In addition, the event-triggered transmission mechanism is introduced to reduce the network communication burden, where the current measurement is transmitted to the remote filter only when it changes greatly compared with the previous one. The aim of this paper is to design a time-varying filter such that, in the presence of the multiple missing measurements, event-triggered transmission mechanism and stochastic nonlinearities, an upper bound of the filtering error covariance is obtained and then minimized by properly designing the filter gain. The explicit form of the filter gain is given in terms of the solutions to two recursive matrix equations. It is shown that the developed filtering scheme is of a recursive form applicable for the online computations. Finally, we provide two illustrative examples to demonstrate the feasibility and applicability of the developed event-triggered filtering scheme.
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