Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.

Trajectory prediction plays an important role in modern air combat. Aiming at the large degree of modern simplification, low prediction accuracy, poor authenticity and reliability of data sample in traditional methods, a trajectory prediction method based on HPSO-TPFENN neural network is established by combining with the characteristics of trajectory with time continuity. The time profit factor was introduced into the target function of Elman neural network, and the parameters of improved Elman neural network are optimized by using the hybrid particle swarm optimization algorithm (HPSO), and the HPSO-TPFENN neural network was constructed. An independent prediction method of three-dimensional coordinates is firstly proposed, and the trajectory prediction data sample including course angle and pitch angle is constructed by using true combat data selected in the air combat maneuvering instrument (ACMI), and the trajectory prediction model based on HPSO-TPFENN neural network is established. The precision and real-time performance of trajectory prediction model are analyzed through the simulation experiment, and the results show that the relative error in different direction is below 1%, and it takes about 42ms approximately to complete 595 consecutive prediction, indicating that the present model can accurately and quickly predict the trajectory of the target aircraft.

Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one-sided, subjective, and low-accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO-GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real-time requirements. Therefore, the model can be effectively used for air target threat assessment.

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