深度复极限学习机在雷达HRRP目标识别中的应用

传统雷达高分辨一维距离像(High-resolution Range Profile,HRRP)目标识别方法只利用目标幅度信息而丢失其相位信息,这势必会造成信息不完备.为解决此问题,提出将深度极限学习机从实数域扩展到复数域,以有效提取复HRRP序列的深层潜在结构信息.同时为更好地保持数据间的邻域信息,将流形正则化引入到...

Application of deep complex extreme learning machine in radar HRRP target recognition

The traditional radar high-resolution range profile(HRRP) target recognition method only uses the target amplitude information and loses its phase information,which will inevitably cause incomplete information.In order to solve this problem,this paper proposes to extend the deep extreme learning machine from the real domain to the complex domain to effectively extract the deep potential structural information of the complex HRRP sequence.At the same time,in order to better maintain the neighborhood relationships between data,manifold regularization is introduced into the training process of network model,and the manifold regularization complex deep extreme learning machine is proposed.Experimental results on radar darkroom measurement data show that the proposed algorithm has better recognition effect and faster training speed than commonly-used deep learning models,which verifies the effectiveness of the algorithm.