Byzantine Resilient Joint Localization and Target Tracking of Multi-Vehicle Systems

作者：Yukang Cui, Yachao Jia, Yuman Li, Jun Shen, Tingwen Huang, Xin Gong · 发表于：IEEE Transactions on Intelligent Vehicles · 年份：2023 · DOI：10.1109/tiv.2023.3250707 · 被引用次数：22 · 研究领域：Distributed Control Multi-Agent Systems、Target Tracking and Data Fusion in Sensor Networks、UAV Applications and Optimization

This work investigates the problem of joint localization and target tracking (JLATT) of multi-vehicle systems (MVSs) in the presence of Byzantine attacks (BAs). For MVSs, there may exist malicious and misbehaving vehicles, called Byzantine vehicles, which act as deceivers and pass false information to their neighbors. For the first time, we formulate a resilient JLATT framework to synthesize the hybrid problems of distributed cooperative localization and flock tracking in the presence of BAs. More specifically, we propose acovariance weighted mean-subsequence-filter(CW-MSF) algorithm to guarantee that the MVS achieves flock tracking w.r.t a target even in the presence of a certain fraction of Byzantine vehicles. Note that this resilient JLATT scheme does not require global information and thus it is fully distributed. The practicality and validity of this novel JLATT framework are verified via an illustrative numerical simulation example.