Information-Driven Cooperative Path Planning for Multi-UAV Bearings-Only Tracking of Clustered Targets

Multi-UAV bearings-only tracking of clustered targets requires coupling target state estimation with platform-motion planning. This paper proposes an information-driven cooperative path-planning method. Targets are partitioned into groups based on spatial distribution, and each UAV primarily tracks its assigned group while providing auxiliary observations to other groups when detected, forming a primary–auxiliary cooperative architecture. Intragroup path planning employs an adaptive-weighting mechanism, where target weights are computed in real time from the trace of the unscented Kalman filter predicted covariance, automatically prioritizing high-uncertainty targets in RRT-guided sampling. Intergroup collaboration adopts a distance-parameterized noise model; bearing-measurement noise is modeled as a function of observation distance, adaptively attenuating the Kalman gain of distant cross-group measurements. The convergence rate of guided sampling is shown to be proportional to the adaptive weight, and the distance-parameterized noise guarantees filtering robustness. Simulations demonstrate that, compared with fixed-weight noncooperative and cooperative planning, the proposed method reduces the RMSE by 91.2% and 15.3%, and shortens the per-step planning time by 30% and 27%, respectively, while eliminating the track-loss events observed in the baselines.