RAI-CoS: Reliability-Calibrated Age-of-Information-Aware Collaborative Sensing for Resilient Multi-UAV Tracking over Intermittent Networks

Multi-UAV tracking can extend coverage by combining aerial observations with reports from fixed monitors and mobile sources. Intermittent links and heterogeneous sources nevertheless create delayed, duplicated, and unreliable evidence, while broad sharing consumes limited bandwidth and energy. This article presents RAI-CoS, a reliability-calibrated and Age-of-Information-aware collaborative sensing framework for this coupled estimation and communication problem. The framework combines context-conditioned source reliability and innovation consistency with generation-time out-of-sequence processing, provenance-aware fusion, receiver-specific information valuation, virtual-queue scheduling, and recurrent decentralized control. We evaluate RAI-CoS in road-constrained multi-target simulations with replayed network traces and supplement the evaluation with Jetson Orin NX decision-stack trace replay. Under the evaluated conditions, RAI-CoS preserves useful tracks through source and network disturbances while reducing unnecessary communication and energy demand. Calibration, fusion, and component analyses connect these outcomes to conservative source assessment, physical-time reconstruction of delayed evidence, correlation-aware fusion, and destination-specific packet selection. Transfer, scaling, and failure analyses further identify where these mechanisms remain effective and where adaptation is required. The findings show that resilient collaborative tracking benefits from treating reliability, time, provenance, and communication cost as one information-control problem.