Probabilistic-Constrained Distributed Filtering for a Class of Nonlinear Stochastic Systems Subject to Periodic DoS Attacks
Probabilistic-Constrained Distributed Filtering for a Class of Nonlinear Stochastic Systems Subject to Periodic DoS Attacks
复制标题
一类遭受周期性 DoS 攻击的非线性随机系统的概率约束分布式过滤
DOI：
10.1109/tcsi.2020.3007953
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发表时间：
2020-12
影响因子：
5.1
通讯作者：
Peng Chen
中科院分区：
文献类型：
--
作者：
Tian Engang;Wang Xinmeng;Peng Chen
本文研究了一类具有状态约束和网络攻击的非线性随机系统的概率约束分布式滤波问题。所考虑的网络攻击是周期性拒绝服务 (DoS) 攻击，其模型是一种周期性脉宽调制 (PWM) 干扰信号。与一些现有的工作不同，通过使用所提出的滤波器设计方法，可以定量地保证滤波误差超过阈值的概率低于一定水平。此外，为了节省有限的带宽资源，设计了事件触发通信方案(ETS)来进行数据传输。所解决问题的目的是设计一个时变分布式滤波器，使得：1）限制在给定椭球上的滤波误差的概率大于指定值； 2) 所获得的椭球阈值在每个时间点的矩阵范数意义上被最小化。为了实现这一目的，利用递归线性矩阵不等式方法并导出充分条件。此外，滤波器参数是根据某些矩阵不等式的解来明确确定的。最后，通过一个示例证明了所提出的分布式滤波策略的可靠性和适用性。
This paper investigates the probabilistic-constrained distributed filtering problem for a class of nonlinear stochastic systems with state constraints and cyber attacks. The considered cyber attacks are periodic denial-of-service (DoS) attacks which are modeled by a kind of periodic pulse-width-modulated (PWM) jamming signals. Different from some existing works, by using the proposed filter design method, the probability of the filtering error exceeding a threshold can be guaranteed below a certain level quantitatively. Furthermore, in order to economize the limited bandwidth resources, an event-triggered communication scheme (ETS) is designed for the data transmission. The aim of the problem addressed is to design a time-varying distributed filters such that: 1) the probability of the filtering error restricted to a given ellipsoid is larger than a specified value; and 2) the obtained ellipsoid threshold is minimized in the sense of matrix norm at each time point. To achieve this purpose, a recursive linear matrix inequality method is utilized and sufficient conditions are derived. Moreover, the filter parameters are explicitly determined in terms of the solution to certain matrix inequalities. Finally, the reliability and applicability of the proposed distributed filtering strategy are demonstrated by an illustrative example.
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