中文摘要：

针对雷达高分辨距离像的平移不变特征——功率谱特征，提出了一种基于Fisher判决率的加权特征压缩方法．该方法利用目标功率谱特征的Fisher判决率迭代搜索最优权向量，并根据最优权值的大小对特征向量降维．与直接使用原始功率谱特征及基于Fisher可分性判据的几种现有的特征压缩方法相比，加权特征压缩方法在降维的同时可提高识别性能，且运算简单，在基于外场实测数据的识别实验中对测试数据具有良好的稳健性。

英文摘要：

This paper proposes a weighted feature reduction method based on Fisher＇ discfiminant ratio（FDR） for a time-shift invariant feature, power spectrum, in radar automatic target recognition using the high-resolution range profile （HRRP）. The proposed weighted feature reduction method uses the FDR vector of the target power spectrum to iteratively search for an optimal weight vector, and reduce feature dimensionality according to the optimal vector. Compared with using the raw power spectrum feature and some existing reduction methods based on Fisher＇s linear discriminant, the proposed weighted weight feature feature reduction method can not only reduce the feature dimensionality, but also improve the recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.