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With the increasing complexity of the air combat environment, the optimal flights path is no longer the only requirement for the path planning system of an unmanned autonomous helicopter (UAH). To enable the UAH path planning system to efficiently handle path planning problems in complex environments, a path planning method is proposed based on satisficing decision-enhanced hybrid swarm intelligence in this article. First, the UAH path planning is modeled as the multiobjective optimization problem. Besides the traditional flight cost of path planning, the performance of UAH and the safety constraints are both considered in this article to establish the fitness function of path planning. Then, a hybrid satisficing decision-enhanced swarm intelligence (HSD-SI) path planning algorithm is proposed based on the satisficing decision method. Through the collaboration and feedback between the hybrid algorithms, the satisfaction enhancement factor is dynamically adjusted so that the HSD-SI algorithm has multiple optimization properties. Thus, the UAH path planning system based on the HSD-SI algorithm can intelligently plan satisfactory flight paths according to the requirements of the mission. Simulation results verify the feasibility and effectiveness of the HSD-SI algorithm in dealing with the UAH path planning problems.

In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multi-source data. Then, the reinforcement learning based data fusion &#x0028 RLBDF &#x0029 method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.