Finite-Time Nonfragile H ∞ Consensus Fuzzy Filtering for Multi-AAV Target Estimation Against Selective-Data-Based Network Attacks
Kunzhong Miao, Chang Wang, Yifeng Niu, Hong Zhang, Huangzhi Yu · IEEE Transactions on Information Forensics and Security · 2025
Complex environments pose significant challenges to the consensus estimation of ground targets by multiple autonomous aerial vehicles (multi-AAVs) with limited sensing capabilities. This paper addresses the design of an$H_{\infty } $consensus fuzzy filter over a finite-time horizon, that is subject to selective network attacks and stochastic incomplete measurements. First, a novel selective-data-based (SDB) network attack model is proposed. Unlike conventional models, this model is constructed from the attacker’s perspective to mimic attacks that target high-value data, thereby maximizing its destructive potential. Second, incomplete measurements, arising from factors such as limited AAV sensing ranges and target motion, are modeled by using a set of random variables to characterize the stochastic nature of data loss. Furthermore, an$H_{\infty } $consensus fuzzy filter is developed to achieve precise consensus estimation of the target with finite-time performance, thereby forming a unified attack-defense architecture. Sufficient conditions for the existence of such a filter are established in the form of linear matrix inequalities (LMIs), from which the filter gains can be derived. Finally, the effectiveness and superiority of the proposed design are validated through both numerical simulations and physical experiments.