Dual-UAV Cooperative Passive Localization Algorithms and Communication-Disturbance Response Evaluation for Maneuvering Targets Under Communication Uncertainty

Abstract

Random communication delay, delay jitter, and packet loss cause remote bearing measurements in dual-UAV bearing-only cooperative passive localization to arrive after their generation times. Direct fusion of these historical measurements with current local measurements misaligns the observation rays, platform geometry, and target state. Within the Cubature Kalman filter (CKF) framework, this article constructs a timestamped cache that preserves historical states, measurements, and platform geometry, and investigates two processing strategies. The Predictive Compensation Strategy (PCS) migrates a historical angular innovation to the current geometry and inflates its covariance. Timestamp-Aligned Replay (TSA) activates the historical record at the measurement generation time, restores the corresponding dual-platform observation geometry, and propagates the corrected state to the current time. The evaluation jointly considers the baseline terminal-window error B, average-delay sensitivity S, delay-jitter sensitivity J, and packet-loss response intensity L. Monte Carlo communication scans show that, under the tested conditions, TSA achieves lower terminal-window localization error and a more favorable overall communication-disturbance response than CDS and PCS, while incurring higher average computational cost.