This paper proposes a geometric-weighted Kalman consensus filter (GW-KCF) that systematically addresses distance-dependent measurement noise effects, unlike traditional methods assuming constant observation noise. The proposed framework introduces dynamic edge weight adaptation across the network to minimize estimation bias, with explicit consideration of occlusion effects through a nonlinear measurement noise model where occlusion is mathematically equivalent to infinite distance. To enhance computational efficiency, we develop a suboptimal algorithm that employs Kalman gain matrix approximation while preserving estimation accuracy. Through Lyapunov stability analysis, we provide rigorous theoretical guarantees for the estimation error dynamics. Numerical simulations reveal that the suboptimal GW-KCF achieves superior consensus accuracy and accelerated convergence speed in scenarios with significant distance-dependent noise variations. Experimental validation with three UAVs tracking a ground target demonstrates the algorithm's robustness against partial node failures, sustaining positioning accuracy within 1 % under sensor dropout conditions.

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.