Peng Xuan, Gao Xunzhang, Zhang Yifan, Li Xiang
College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2017 Jul 20;17(7):1675. doi: 10.3390/s17071675.
This paper proposes a new feature learning method for the recognition of radar high resolution range profile (HRRP) sequences. HRRPs from a period of continuous changing aspect angles are jointly modeled and discriminated by a single model named the discriminative infinite restricted Boltzmann machine (Dis-iRBM). Compared with the commonly used hidden Markov model (HMM)-based recognition method for HRRP sequences, which requires efficient preprocessing of the HRRP signal, the proposed method is an end-to-end method of which the input is the raw HRRP sequence, and the output is the label of the target. The proposed model can efficiently capture the global pattern in a sequence, while the HMM can only model local dynamics, which suffers from information loss. Last but not least, the proposed model learns the features of HRRP sequences adaptively according to the complexity of a single HRRP and the length of a HRRP sequence. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database indicate that the proposed method is efficient and robust under various conditions.
本文提出了一种用于雷达高分辨距离像（HRRP）序列识别的新特征学习方法。来自一段连续变化视角的HRRP通过一个名为判别式无限受限玻尔兹曼机（Dis-iRBM）的单一模型进行联合建模和判别。与常用的基于隐马尔可夫模型（HMM）的HRRP序列识别方法相比，后者需要对HRRP信号进行高效预处理，而本文提出的方法是一种端到端的方法，其输入是原始HRRP序列，输出是目标的标签。所提出的模型能够有效地捕捉序列中的全局模式，而HMM只能对局部动态进行建模，存在信息损失问题。最后但同样重要的是，所提出的模型根据单个HRRP的复杂度和HRRP序列的长度自适应地学习HRRP序列的特征。在移动与静止目标获取与识别（MSTAR）数据库上的实验结果表明，所提出的方法在各种条件下都是高效且稳健的。
