摘要
针对线性判别分析(LDA)的"小样本"和要求数据须服从高斯分布的问题,提出一种基于非参数化最大间隔准则(NMMC)的雷达目标识别方法.首先,利用自相关小波变换提取目标高分辨距离像(HRRP)的非平稳特征,将其与HRRP原信号一起作为目标的分类特征,利用NMMC实现特征提取;然后,通过支持向量机进行分类.NMMC在解决小样本问题的同时,松弛了对数据分布的类高斯要求.最后,基于5种飞机高分辨距离像数据的仿真实验验证了所提出方法的有效性.
As an effective feature extraction method in radar target high-resolution range profile（HRRP） recognition community, linear discriminant analysis（LDA） faces two main shortcomings which are referred to as small sample size problem and Gaussian-like assumption respectively. Therefore, a radar target recognition method based on nonparametric maximum margin criterion（NMMC） is proposed. Firstly, the non-stationary characters of HRRP are extracted by the auto- correlation wavelet transform, which are used as the target classification characteristics together with HRRPs. Then the classification characteristics are extracted by using the NMMC algorithm, and support vector machine（SVM） classier is used for target recognition. NMMC solves the small sample size problem and relaxes the requirement of Gaussian distribution assumption in LDA. Finally, simulation results based on a HRRP dataset of five aircraft models show the effectiveness of the proposed approach.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第12期1835-1839,1845,共6页
Control and Decision
基金
装备预研重点基金项目(N0601-041)
中电集团第14研究所院士基金项目(2008041001)
作者简介
吴杰（1977-），女，博士生，从事雷达自动目标识别、信号处理等研究；
周建江（1962-），男，教授，博士生导师，从事雷达信号处理、目标识别等研究．
