A Residual PPO Algorithm Based on Blended Generalized Proportional Navigation for Terminal UAV Interception in Three-Dimensional Asymmetric Confrontations

Unauthorized low-altitude UAVs can challenge conventional fixed-parameter interception algorithms through agile maneuvers. This study develops a three-dimensional one-on-one terminal-interception simulation environment that incorporates protected-zone penetration, a within-step geometric interception criterion, and kinematic constraints. The intruder, denoted as the red UAV, combines six physically interpretable maneuver templates to generate structured evasive penetration behavior. The defender, denoted as the blue UAV, augments blended generalized proportional navigation (B-GPN) with a bounded residual corrective acceleration produced by deep reinforcement learning, thereby forming a hybrid architecture that combines a geometry-based nominal guidance command with reward-driven bounded compensation. In standardized tests on 1000 unseen scenarios, the implemented residual PPO pipeline increased the interception rate from 63.8% for nominal guidance to 94.1% (95% Wilson interval: 92.46–95.40%) and maintained at least 88.0% interception under the tested control-delay, kinematic, and noise perturbations. It also achieved the highest interception rate among the evaluated residual-learning implementations under both the stable-configuration comparison and the auxiliary task-side-controlled check; this result is limited to the reported implementations and is not a general ranking of algorithm families. These findings indicate that bounded residual learning can compensate for structural limitations of conventional guidance under the evaluated conditions.