非线性正则子空间法雷达目标一维距离像识别

Recognition of Radar Target Rangeprofiles Based on Nonlinear Canonical Subspace

雷达目标识别中,目标一维距离像的分布常表现出明显的非线性和复杂性,这时经典的线性子空间方法的识别性能会有所下降.为此,本文提出非线性正则子空间法,通过对一维距离像进行非线性变换,使在原有空间线性不可分的一维距离像模式在高维空间有望具有线性可分性,从而提高目标的识别性能.对实测飞机数据的实验结果表明该方法的有效性.

The novel subspace method is proposed. Firstly, the input rangeprofiles are transformed into high dimension feature space by nonlinear transformation. Then the canonical subspace is constructed in the high dimension feature space to extract feature for improving performance of classification. Finally, the minimum distance classifier is used to classify aircraft targets. The experimental results on the real data of three kinds of aircrafts show the efficiency of the proposed method.