Event-Triggered Distributed Cubature Kalman Filtering Algorithm With Stealthy Attacks Over Sensor Networks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-06 DOI:10.1109/TSIPN.2025.3525977

Yinping Ma;Zhoujian Ma;Yinya Li;Yuan Liang

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引用次数: 0

Abstract

This article investigates the security problem of distributed state estimation for nonlinear systems subject to stealthy attacks and limited energy. First, a novel detection strategy for a nonlinear information consensus filter is designed to resist the stealthy adversary which can modify the data transmitted through the wireless network. Unlike existing attack detection strategies, the proposed defense strategy is capable of simultaneously verifying the authenticity of the received local estimate and error covariance. Afterward, considering the limited communication resources, an event-triggered distributed cubature Kalman filtering algorithm with the aforementioned detection strategy is presented to fuse the local information. This algorithm can reduce communication consumptions and guarantee good estimation precision for sensor networks with stealthy attacks and limited energy. Subsequently, the stability properties of the developed nonlinear filtering algorithm are presented. Finally, two examples are given to demonstrate the effectiveness of the proposed approach.

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本文研究了非线性系统在隐身攻击和有限能量条件下分布式状态估计的安全问题。首先，针对非线性信息共识滤波器设计了一种新型检测策略，以抵御隐形对手修改通过无线网络传输的数据。与现有的攻击检测策略不同，所提出的防御策略能够同时验证接收到的本地估计值和误差协方差的真实性。随后，考虑到有限的通信资源，提出了一种事件触发的分布式立方卡尔曼滤波算法，并结合上述检测策略来融合本地信息。该算法可以减少通信消耗，并保证在隐身攻击和能量有限的传感器网络中具有良好的估计精度。随后，介绍了所开发的非线性滤波算法的稳定性。最后，给出了两个例子来证明所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.