In close-range dynamic UAV tracking, the sharp decrease in relative distance and rapidly changing relative-motion conditions require UAVs to execute highly dynamic maneuvers. Traditional autonomous decision-making systems struggle with the curse of dimensionality in continuous action spaces or suffer from strategy-level rigidity when using predefined discrete maneuver primitives. This paper aims to resolve these limitations by developing a dimension-reduced yet highly continuous decision-making framework. We propose a hierarchical deep reinforcement learning architecture based on a geometric pursuit-strategy action space. The top-level Proximal Policy Optimization agent evaluates the relative-motion state to output discrete guidance-mode commands: lag pursuit, lead pursuit, or pure pursuit. A mid-level guidance translator converts these intents into continuous flight reference commands based on angular geometry and energy maneuverability. The bottom-level guidance translator utilizes a high-fidelity JSBSim fixed-wing aircraft flight-dynamics model for precise aerodynamic control. Monte Carlo simulations and comparative experiments across representative initial postures show that the proposed framework improves training convergence compared with a conventional continuous-control PPO baseline and achieves more stable high-level guidance-mode selection than a Double-DQN baseline. In simulation tests under predefined geometric tracking-success criteria, the model achieved a 91.5% success rate in initially favorable configurations and a 64.0% success rate when starting from a challenging configuration. By abstracting complex maneuvers into geometric pursuit strategies, this hierarchical framework lowers exploration dimensionality while maintaining the continuous kinematic logic of flight trajectories, providing an interpretable and simulation-validated decision-making framework for UAV close-range dynamic tracking and autonomous flight control.
Lai et al. (Fri,) studied this question.