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Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors—freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)—into a single multiplicative score qij, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, On3+nm per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI–covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08–74.24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04–74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work.
Cao et al. (Wed,) studied this question.