基于字典学习的雷达高分辨距离像目标识别

Radar HRRP target recognition based on dictionary learning

提出一种基于字典学习的雷达高分辨距离像(HRRP)目标识别算法。该算法依据对测试样本的信噪比估计,可以自适应地确定测试阶段稀疏分解的稀疏度系数。相比于传统识别算法,文中算法对目标的识别性能更好,且对噪声的鲁棒性更强。另外,文中算法可以在只训练部分角域数据(不完备训练集)的条件下较好地识别全角域数据,可应用于HRRP数据库的扩展。基于实测数据的识别试验验证了该算法的有效性。

The dictionary learning based radar high-resolution range profile（HRRP） target recognition method is proposed in this paper. The method can adaptively select the sparse decomposition coefficients based on the estimated test noise level. Compared with the traditional HRRP recognition methods, the proposed algorithm has a higher recognition rate and is more robust to the test noise environment. Furthermore, this method can obtain satisfactory performance even with the limited HRRP training samples from partial target-aspect angles （i. e. , the training dataset is incomplete）, thereby it can be used for HRRP dataset extension. The experiments based on measured HRRP data validate the proposed method.