采用多特征联合学习的噪声稳健HRRP识别方法

为提高低信噪比条件下雷达目标高分辨一维距离像的识别性能，提出一种采用多特征联合学习的噪声稳健目标识别方法．该方法利用核函数实现对稀疏与低秩的联合表示，用来提取目标高分辨一维距离像的局部特征与全局特征．在训练阶段，利用联合可分性分析多分类器综合结构字典学习方法对特征提取字典进行优化，从而提高特征向量的可分性；在测试阶段，利用对消原理对噪声进行自适应抑制，实现噪声干扰下的稳健识别．利用实测数据进行实验，结果表明该方法可有效地对被噪声污染的目标高分辨一维距离像进行恢复，并提高低信噪比下的目标识别准确率，且满足实际应用中的实时性要求．由此可见，该方法可以有效地提高高分辨一维距离像目标识别系统在低信噪比下的总体性能．

Noise-robust multi-feature joint learning HRRP recognition method

In order to improve the recognition performance under low signal-to-noise ratio (SNR) conditions, a novel method is proposed for radar target high range resolution profiles (HRRP). This method achieves good recognition performance based on multi-feature joint learning for noisy HRRPs. The framework of this method is constructed based on sparse representation and low-rank representation, which are applied to extract the local and global features of target HRRPs. In the training stage, a feature extraction dictionary is produced based on the joint learning structured analytical discriminative dictionary method to improve the recognition performance. The cancellation method is implemented for noise suppression in the testing stage. Experimental results on the measured HRRP data demonstrate that the proposed method can significantly improve the overall recognition performance for HRRP testing samples under relatively low SNR conditions with a satisfactory real-time ability.