基于混沌遗传-支持向量机的空地攻击型无人机作战效能评估

Operational Effectiveness Evaluation of Electronic Warfare UAV Based on Chaotic Particle Swarm Optimization and Support Vector Machine

空地攻击型无人机的作战效能评估在未来战场“去功能化”打击中具有重要的意义.在对空地攻击型无人机作战效能实时评估时,影响作战效能的评价指标体系因素相对复杂,建立作战效能评估模型存在非线性特点,因此本文引入了具有较强鲁棒性的支持向量回归机算法进行作战效能评估,在构建空地攻击型无人机评估体系基础上,利用混沌系统-遗传算法对支持向量机惩罚因子等参数进行优化,保证战场实时环境下效能评估的有效性和效率,通过实例分析,混沌遗传-支持向量机模型能够准确地对空地攻击型无人机进行有效的作战效能评估,具有良好的鲁棒性.

The evaluation of the combat effectiveness of air-to-ground attack UAVs is of great significance in the future battlefield "disabled"strike.In the real-time evaluation of the combat effectiveness of air-to-ground attack UAVs,the factors affecting the evaluation index system of the combat effectiveness are relatively complex,and the establishment of the combat effectiveness evaluation model has nonlinear characteristics.Therefore,this paper introduces a robust support vector regression machine.The algorithm is used to evaluate the combat effectiveness.Based on the construction of the air-ground attack UAV evaluation system,the chaotic system-genetic algorithm is used to optimize the parameters such as the support vector machine penalty factor to ensure the effectiveness and efficiency of the effectiveness evaluation in the real-time battlefield environment.Through case analysis,the chaotic genetic-support vector machine model can accurately evaluate the combat effectiveness of air-to-ground attack UAVs and has good robustness.