基于AEPSO-SVM算法的雷达HRRP目标识别

Radar HRRP target recognition based on AEPSO-SVM algorithm

针对雷达自动目标识别中的高分辨距离像(high resolution range profile,HRRP)识别问题,提出自适应进化粒子群(adaptive evolution particle swarm optimization,AEPSO)算法优化支持向量机(support vector machine,SVM)的目标分类识别方法。该算法利用非线性自适应惯性权重的调整以适应粒子寻优的非线性变化过程,采用分阶段调节加速因子增强粒子在进化过程中的学习能力,通过引入局部搜索算子在增加粒子多样性的同时有效避免了粒子陷入局部最优陷阱。通过改进的PSO算法优化SVM参数,建立分类识别器模型。将该AEPSO-SVM模型应用到雷达HRRP目标识别中,实验结果表明,该算法对于高分辨雷达目标识别精度高、鲁棒性强。

Radar high resolution range profile (HRRP) identification in radar automatic target recognition based on an adaptive evolutionary particle swarm optimization (AEPSO) optimizing support vector machine (SVM) target classifier method is proposed.To enhance the learning ability of particles in the evolution process,the proposed method,using the staged adjustment acceleration factor,adapts to the nonlinear variation of the particle optimization by an adaptive inertia weight.According to introducing local search operators,the particle diversity,to avoid the local optimal trap,is increased effectively.The SVM parameters are optimized by the improved PSO algorithm to establish a classification recognizer model.The AEPSO-SVM model is applied to radar HRRP target recognition,and the simulation results demonstrate that the proposed method has better accuracy and robustness.