中文摘要：

基于字典学习算法的信号稀疏表示被广泛应用于信号处理领域。由于字典原子间存在冗余性,求解信号的稀疏表示会受到观测信号中扰动分量的影响,从而带来表示的不确定性,不利于雷达高分辨距离像（HRRP）目标识别任务。针对这一问题,该文提出一种稳健字典学习（SDL）算法,通过边缘化信号丢失,构建稳健损失函数用于学习自适应字典。该算法利用距离像在散射点不发生越距离单元走动的方位帧内具有结构相似性,约束临近训练样本间稀疏表示的非零元素位置相同,并通过结构化稀疏约束选择最优子字典用于测试样本的分类。基于实测HRRP数据的实验结果验证了所提算法的有效性。

英文摘要：

The sparse representation of signal via dictionary learning algorithms is widely used in signal processing field. Since there is redundancy in the new space defined by overcomplete dictionary atoms, the problem of finding sparse representations may bring the uncertainty and ambiguity in the presence of unknown amplitude perturbations, which is unfavorable to radar High Resolution Range Profile （HRRP） target recognition task. To deal with this issue, this paper proposes a novel algorithm called Stable Dictionary Learning （SDL）, which constructs a robust loss function via marginalizing dropout to learn a stable adaptive dictionary. The algorithm considers the structure similarity among the adjacent HRRPs without scatterers＇ motion through range cells, and enforces the constraints that the sparse representations of adjacent HRRPs should have the same supports. Moreover, SDL utilizes the structured sparse regularization learned in the training phase to automatically select the optimal sub-dictionary basis vectors, which is used for the classification of the test sample. Experimental results on measured radar HRRP dataset validate the effectiveness of the proposed method.