基于核主分量相关判别分析特征提取方法的目标HRRP识别

Kernel Principal Component Correlation and Discrimination Analysis Feature Extraction Method for Target HRRP Recognition

为有效提高雷达高分辨1维距离像目标识别系统的总体性能,需要对目标高分辨1维距离像进行特征提取,以得到具有最小信息损失、高可分性且低维度的目标特征,为实现该目的提出一种基于核主分量相关判别分析的特征提取算法。该算法基于目标高分辨1维距离像的统计特性,通过对核主分量分析中核函数的选择,实现对不同类型距离单元的特征提取。同时综合线性判别分析与典型相关分析理论构建新的准则函数,以实现特征空间中类内相关性与类间差异性最大化,同时减少目标特征中的冗余信息。利用实测数据进行实验,结果表明该方法提高了特征向量的可分性,降低了特征向量的维度,并且对该算法在不同强度杂波下的识别性能进行了分析,实验结果表明,该方法可以有效的提高目标高分辨1维距离像目标识别系统的总体性能。

For radar High Resolution Range Profile （HRRP） automatic target recognition, the features should be extracted with sufficient target information, high discrimination, noise robustness, and low feature vector dimension. However, radar HRRP recognition suffers from insufficient amount of information and low discrimination feature, besides the radar recognition system also need the ability of real-time processing with low dimension. To obtain features with merits of low-dimension and high-discrimination, a novel feature extraction method is designed for radar high range resolution profile, namely Kernel Principal Component Correlation and Discrimination Analysis （KPCCDA）. With the proposed method, the statistical characteristics of different scatter range cells can be effectively used by Kernel Principal Component Analysis （KPCA）. And the within-class correlation and between- class discrimination are maximized with linear discrimination analysis and canonical correlation analysis used. Besides, the redundancy and dimensionality of the feature vectors are reduced, yielding a lowered computational complexity to meet the storage requirement in practical radar target recognition. Experimental results with measured data validate the efficiency of the proposed method.