基于CHCQPSO-LSSVM的空战目标威胁评估

针对传统评估方法存在的模型精度低、结构复杂、无法进行实时动态威胁评估等问题,提出了一种基于最小二乘支持向量机(LSSVM)的空战目标威胁评估方法。首先,对空战特征数据进行威胁指数分析,结合专家评判构建威胁评估样本库;然后,采用交叉杂交的混沌量子粒子群算法(CHCQPSO)对LSSVM中的正则化参数与核函数参数进行寻优,并与经典PSO、BP神经网络、网格法模型进行对比分析;最后,用优化后的LSSVM模型实现空战目标实时动态威胁评估。仿真结果表明,所提方法评估精度高、用时短,为空战目标威胁评估提供了一种新思路。

A Target Threat Assessment in Air Combat Based on CHCQPSO-lSSVM

Aimed at the problems that the traditional evaluation methods are low in evaluation precision, complex in structure of the model, and is not available in the real-time dynamic threat assessment, an air combat threat assessment method based on least squares support vector machine (LSSVM) is proposed. Firstly, the threat index of air combat characteristic data is analyzed, and the expert evaluation is used to build the threat assessment sample base. Then, cross hybridization chaos quantum particle swarm optimization(CHCQPSO) algorithm is used to optimize the regularization parameter and kernel function parameter in LSSVM, and the results are compared with those of the classical PSO, BP neural networks and mesh models. Finally, the optimized LSSVM model is used to realize the real-time dynamic threat assessment of air combat targets. The simulation results show that the proposed method is high in accuracy and short in time required, thus providing a new idea for evaluating the threat of air combat targets.