中文摘要：

高分辨距离像目标识别算法很多,但利用高分辨距离像（HRRP）稀疏特点进行识别的方法却不多。为此,提出一种基于结构划分过完备字典完成雷达一维距离像稀疏分解,进而实现目标识别的算法。该算法首先依据字典原子的结构特点对其进行划分,简化字典表述的同时减少了原子数据存储量;随后,采用遗传匹配追踪算法（GAMP）对一维距离像训练样本进行稀疏分解以获得类别字典;最后,根据类别字典分析测试样本的重构误差实现目标识别。仿真实验证明,文中算法简洁、识别率高,即便受到噪声干扰依然能稳健地识别目标。

英文摘要：

There are many radar target recognition algorithms based on high range resolution profile （HRRP）, but less of them employ the sparseness of HRRP samples. Thus, an redundant dictionary and a fast sparse representation algorithm are used to implement radar target recognition here. First, a Gabor redundant dictionary was partitioned by the characteristics of the atoms in it. By doing this, the atoms storage was decreased and the dictionary was generated faster. Then, the genetic algorithm-matching pursuit algorithm（GAMP） was used to product the training samples＇ taxonomic dictionaries quickly. Finally, the reconstruction errors of testing samples were calculated to recognize the targets. The simulations show that this algorithm has the advantages of concise, higher recognition rate and good robustness.