Dynamic RCS-Based Deep Reinforcement Learning Path Planning for Fixed-Wing UAVs in Complex Environments

This paper investigates the low-altitude penetration path planning problem for a fixed-wing UAVs in complex battlefield environments with mountainous terrain and radar threats. First, a radar detection probability model and a dynamic radar cross section (RCS) model are established to characterize target exposure risk. Then, a deep reinforcement learning-based (DRL) path planning framework is developed, in which the Soft Actor-Critic (SAC) algorithm is employed to solve the penetration task under dynamic RCS constraints. A multi-objective reward design is constructed to jointly account for target-reaching progress, radar-threat avoidance, obstacle avoidance, and terminal task completion. Simulations show dynamically feasible flight paths under the adopted models. HIL tests conducted in the corresponding five-radar and two-radar numerical scenarios support onboard path planning, waypoint transmission, and functional closed-loop execution. Comparisons reveal a trade-off among penetration time, modeled radar exposure, and computation, with SAC achieving the shortest mean penetration time among successful trials.