基于粒子群优化算法的雷达目标相关匹配识别  

Radar Target Correlation Matching Recognition Based on Particle Swarm Optimization

作　　者：陈治礼[1] 李明之[1] 

机构地区：[1]北京大学信息科学技术学院,北京100871

出　　处：《微波学报》2010年第S2期308-311,共4页Journal of Microwaves

基　　金：国家自然科学基金(60401012;60825102)

摘　　要：粒子群优化算法易实现,鲁棒性强,对复杂线性和非线性问题均具有较强的寻优能力,是一种高性能智能优化算法。文中采用高分辨率一维距离像和宽带去极化系数作为目标特征矢量,基于相关匹配算法设计分类器,并针对相关匹配算法计算量过于庞大的问题,引入粒子群算法对分类器搜索最大相关匹配系数的过程进行优化,极大地提高了分类器的性能和效率。The particle swarm optimization(PSO) is a high performance optimization algorithm.It is easy to implement with strong robustness,and nearly always can be used to solve complicated linear and nonlinear optimization problems. High-resolution range profile(HRRP) and broadband depolarization coefficient were extracted as target feature vector in this paper.A classifier based on the correlation matching algorithm was designed.PSO algorithm was introduced to optimize the classifier in order to solve the problem of large workload.Simulation results show that PSO can greatly improve the capability of classifier and is more efficient.

关 键 词：粒子群优化算法 雷达目标识别 相关匹配分类器 高分辨率一维距离像 宽带去极化系数 

分 类 号：TN957.51[电子电信—信号与信息处理]