Informative Planning With Attention-Based Hybrid Belief Reinforcement Learning for Aerial Multi-Target Search and Tracking

Autonomous multi-target search and tracking (SaT) under limited sensing fields of view requires balancing the maintenance of precise estimates for visible targets with the recovery of those lost to intermittent observations. To address this, we present HyBE-RL, an integrated active perception framework that synergizes a hybrid belief estimator (HyBE) with an attention-driven reinforcement learning planner. The estimation layer employs a visibility-dependent mechanism to leverage negative information from non-detections, coupling parametric tracking with non-parametric search to maintain effective priors during observation gaps. Building on this representation, the planner utilizes a spatial-uncertainty attention mechanism to map continuous belief directly to optimal control actions, explicitly overcoming the discretization artifacts and heuristic sub-optimality inherent in conventional geometric planning. To facilitate robust learning, we incorporate a sampling-based geometric planner to provide expert-guided reward shaping and function as a runtime safety shield. Comprehensive simulations and real-world uncrewed aerial vehicle (UAV) experiments validate that the proposed approach not only outperforms standard baselines but also surpasses its geometric teacher in complex environments, achieving reduced tracking errors and superior target recovery.