JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2024 Vol.25 No.8 P.1110-1122

An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems

Author(s): Yuru HU, Wangyan LI, Lifeng WU, Zhensheng YU
Affiliation(s): College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
Corresponding email(s): wangyan_li@usst.edu.cn
Key Words: Extended Kalman consensus filtering, Hypothesis testing, Rectification strategy, Multi-UAV tracking

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Abstract: This study investigates how the events of deception attacks are distributed during the fusion of multi-sensor nonlinear systems. First, a deception attack with limited energy (DALE) is introduced under the framework of distributed extended Kalman consensus filtering (DEKCF). Next, a hypothesis testing-based mechanism to detect the abnormal data generated by DALE, in the presence of the error term caused by the linearization of the nonlinear system, is established. Once the DALE is detected, a new rectification strategy can be triggered to recalibrate the abnormal data, restoring it to its normal state. Then, an attack-resilient DEKCF (AR-DEKCF) algorithm is proposed, and its fusion estimation errors are demonstrated to satisfy the mean square exponential boundedness performance, under appropriate conditions. Finally, the effectiveness of the AR-DEKCF algorithm is confirmed through simulations involving multi-unmanned aerial vehicle (multi-UAV) tracking problems.

一种对攻击韧性的分布式一致性扩展卡尔曼滤波算法及其在多无人机追踪问题中的应用

胡玉如，李忘言，吴励锋，宇振盛
上海理工大学理学院，中国上海市，200093
摘要：本文研究了非线性系统下多传感器融合过程中发生的欺骗攻击事件。首先，在分布式一致性扩展卡尔曼滤波（DEKCF）框架下引入一种能量受限的欺骗攻击（DALE）。然后，建立一种基于假设检验的检测机制，在存在误差项的情况下，对非线性系统进行线性化处理以检测DALE产生的异常数据。为有效缓解DALE对局部节点预测值的不良影响，分析设计了一种状态估计校正策略，用于重新校准由DALE引起的异常数据。在此基础上，提出了一种对攻击韧性分布式一致性扩展卡尔曼滤波（AR-DEKCF）算法，并在适当的条件下证明了其融合估计误差满足均方指数有界性能。最后，通过多无人机追踪问题的仿真实验验证了该算法的有效性。

关键词：一致性扩展卡尔曼滤波；假设检验；纠正策略；多无人机追踪

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