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杨爱武 , 李战武 , 李宝 , 等 . 基于动态变权重的空战态势评估 [J ] . 兵工学报 , 2021 , 42 ( 7 ): 1553 - 1563 .

YANG A W , LI Z W , LI B , et al. Air combat situation assessment based on dynamic variable weight [J ] . Acta Armamentarii , 2021 , 42 ( 7 ): 1553 - 1563 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.07.023 http://doi.org/10.3969/j.issn.1000-1093.2021.07.023 In air combat situation assessment, the situation assessment model does not adequately describe the high dynamics and game process of air combat, and the constant weight assessment situation leads to "state imbalance". A situation assessment method based on dynamic variable weight is proposed. First, based on the characteristics of air combat, the maneuver strength and maneuver potential models are established to quantify the tactical intent and enrich the situation assessment model, and solve the problem of deviation between the situation assessment result and the actual air combat situation due to the fixed value of tactical intent. The objective constant weight vector is calculated based on the criteria importance through intercrieria correlation method, and the equilibrium function is constructed from the analysis of the air combat evaluation index influencing factors, and the indicator state variable weight vector is obtained. The variable weight of the indicator is calculated by the Hadamard product of the constant weight vector and the state variable weight vector, and solve the problem of "state imbalance" in the evaluation situation with constant weight. The simulated results show that the proposed method is more objective in situation assessment and conforms to the characteristics of actual air combat.

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史国庆 , 武凡 , 张林 , 等 . 一种基于改进粒子群优化算法的机载多传感器任务分配方法 [J ] . 西北工业大学学报 , 2018 , 36 ( 4 ): 722 - 727 .

SHI G Q , WU F , ZHANG L , et al. An airborne multi-sensor task allocation method based on improved particle swarm optimization algorithm [J ] . Journal of Northwestern Polytechnical University , 2018 , 36 ( 4 ): 722 - 727 . (in Chinese) DOI: 10.1051/jnwpu/20183640722 http://doi.org/10.1051/jnwpu/20183640722 https://www.jnwpu.org/10.1051/jnwpu/20183640722 https://www.jnwpu.org/10.1051/jnwpu/20183640722 The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.

潘超 , 韩笑 , 张瑞甫 , 等 . 耗能增效惯容系统的自适应权重粒子群优化 [J/OL ] . 振动工程学报 : 1 - 10 [2022-01-05 ] . http:∥kns.cnki.net/kcms/detail/32.1349.TB.20211026.0919.002.html http:∥kns.cnki.net/kcms/detail/32.1349.TB.20211026.0919.002.html .

PAN C , HAN X , ZHANG R F , et al. Adaptively weighted particle swarm optimization for damping enhanced inerter system [J/OL ] . Journal of Vibration Engineering : 1 - 10 [2022-01-05 ] . http:∥kns.cnki.net/kcms/detail/32.1349.TB.20211026.0919.002.html http:∥kns.cnki.net/kcms/detail/32.1349.TB.20211026.0919.002.html . (in Chinese)

朱建文 , 赵长见 , 李小平 , 等 . 基于强化学习的集群多目标分配与智能决策方法 [J ] . 兵工学报 , 2021 , 42 ( 9 ): 2040 - 2048 .

ZHU J W , ZHAO C J , LI X P , et al. Multi-target assignment and intelligent decision based on reinforcement learning [J ] . Acta Armamentarii , 2021 , 42 ( 9 ): 2040 - 2048 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.09.025 http://doi.org/10.3969/j.issn.1000-1093.2021.09.025 A reinforcement learning-based swarm intelligent decision-making method of cooperative multi-target attack under high-dynamic situation is proposed. The composite evaluation criteria of attack performance is established, including the evaluation of attack superiority based on relative motion information and the threat evaluation based on the inherent information of target. To evaluate the attack-defence effectiveness, a cost-effectiveness ratio index is designed by combining attack performance, penetration probability and attack cost together. In addition, a multi-target decision-making architecture based on reinforcement learning is constructed, and an action space with allocation vectors as basic elements and a state space based on quantified performance indicators are designed. Q-Learning is employed to make intelligent decisions on cooperative attack plans, including missile selection and target assignment. The simulated results show that reinforcement learning can achieve multi-target online decision-making with the optimal offensive and defensive effectiveness, and its computational efficiency has more obvious advantages than that of particle swarm optimizer.