Distributed Counter-UAV Early Warning: Acoustic–Visual Information Consensus and Fuzzy–Bayesian Threat Assessment

This paper presents a distributed counter-UAV early-warning and response-decision support framework for low-altitude UAV defense. Acoustic–visual edge nodes generate local state packets and exchange compact information-filter parameters through Multi-Target Information Consensus (MTIC), avoiding centralized fusion and reducing payload bandwidth. The MTIC naïvety-handling mechanism is extended to heterogeneous acoustic–visual sensing, improving robustness to partial observations, packet loss, and node disconnection. A Fuzzy–Bayesian threat-assessment layer converts fused distance, velocity, and heading cues into interpretable response recommendations with calibrated confidence. Implemented on ROS 2/Fast DDS with tiered QoS, software-assisted IEEE 1588 synchronization, and Preempt-RT scheduling, the framework achieves within about 5% of centralized accuracy while reducing payload bandwidth by up to about 97% relative to the main centralized baseline. Simulation and hardware-in-the-loop tests on three- and five-node mesh topologies show software-assisted sub-millisecond synchronization (200–500 μs offset), bounded latency, gradual AUC degradation under association mismatch, and end-to-end feasibility under controlled packet loss. Overall, the system provides a resilient, deployment-oriented architecture for distributed C-UAV early warning.