对传感器欺骗具有弹性的有向图分布式估计
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing
AI总结：
针对有向通信网络中部分传感器被攻击者篡改的问题，提出基于权重平衡与 consensus+innovations 的 REWB 算法，在受攻击智能体少于半数时保证所有智能体正确估计动态参数。
摘要：
本文研究多智能体系统在有向通信网络上对未知动态参数进行分布式估计的问题，其中智能体的传感器面临对抗性攻击。攻击者的攻击方式是破坏部分智能体的传感器测量值，而这些智能体的通信和信息处理能力仍保持正常。为确保所有智能体（包括正常智能体和遭受攻击的智能体）都能正确估计参数值，本文提出了通过权重平衡实现弹性估计的 Resilient Estimation through Weight Balancing（REWB）算法。REWB 算法保证弹性估计所需的唯一条件是：在任意给定时刻，遭受攻击的智能体数量少于智能体总数的一半。本文利用有向图权重平衡的概念，以及用于线性估计的 consensus+innovations（共识＋创新）方法，讨论了 REWB 算法的构建。数值仿真展示了该算法在不同对抗攻击条件下于有向图上的性能。
英文摘要：
This paper addresses the problem of distributed estimation of an unknown dynamic parameter by a multi-agent system over a directed communication network in the presence of an adversarial attack on the agents' sensors. The mode of attack of the adversaries is to corrupt the sensor measurements of some of the agents, while the communication and information processing capabilities of those agents remain unaffected. To ensure that all the agents, both normal as well as those under attack, are able to correctly estimate the parameter value, the Resilient Estimation through Weight Balancing (REWB) algorithm is introduced. The only condition required for the REWB algorithm to guarantee resilient estimation is that at any given point in time, less than half of the total number of agents are under attack. The paper discusses the development of the REWB algorithm using the concepts of weight balancing of directed graphs, and the consensus+innovations approach for linear estimation. Numerical simulations are presented to illustrate the performance of our algorithm over directed graphs under different conditions of adversarial attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
