Collaborative strategy for hybrid actions of radar modes and maneuver decisions under observation errors

The rapid advancement of airborne avionics has driven modern air combat to rely heavily on information-centric operations, with radar serving as a primary tool for information acquisition and playing a critical role in air combat. However, existing research on air combat strategies often overlooks the impact of different radar operating modes on maneuvering strategies, as well as the challenges posed by learning strategies under observational disturbances. To address these gaps, this study investigates the problem of hybrid actions decision-making for radar modes and maneuver decisions in the presence of observational errors. Specifically, the characteristics of various radar operating modes are analyzed and modeled, followed by an exploration of the convergence process of reinforcement learning strategies under observational disturbances. To mitigate the instability and volatility in strategy learning caused by observation errors, Entropy-Decoupling-Noisy-net Proximal Policy Optimization-Advanced (EDN-PPOA) algorithm is proposed, which significantly enhances the robustness and exploratory capability of the model. Simulation results demonstrate that the proposed algorithm effectively achieves coordinated tactical integration of radar modes and maneuvers in complex hybrid action spaces, producing flexible tactical strategies that outperform expert-designed heuristics. Furthermore, compared to the existing algorithms, the proposed method exhibits superior stability and robustness in noisy observational environments, providing a reliable technical foundation for intelligent decision-making in complex adversarial scenarios.