基于稳健变分自编码模型的雷达高分辨距离像目标识别算法

对于雷达高分辨距离像的识别问题，传统深层网络通常忽略了HRRP自身的目标特性，不利于学习有效的分类特征，导致其识别性能受到限制.针对这一问题，本文提出了一种基于稳健变分自编码模型的目标识别算法.该算法结合HRRP数据特性，利用平均像在散射点不发生越距离单元走动的方位帧内具有稳健物理特性的性质，基于变分自编码器构建了稳健变分自编码模型.该模型不仅能够获取稳健有效的识别特征，而且在一定程度上保存了数据的帧内结构信息，较大地提高了目标的平均识别率.基于实测HRRP数据验证了所提算法的有效性.

Robust Variational Auto-Encoder for Radar HRRP Target Recognition

Traditional deep networks used for radar High-Resolution Range Profile (HRRP) target recognition usually ignore the inherent characteristics of the target,which results in the limited capability to learn effective features for classification task.To address this issue,a novel nonlinear feature learning method,called Robust Variational Auto-Encoder model (RVAE) is proposed.According to the stable physical properties of the average profile in each HRRP frame without migration through resolution cell,RVAE is developed based on variational auto-encoder,and such model is able to not only explore the latent representations of HRRP but also reserve structure characteristics of the HRRP frame.We use the measured HRRP data to show the effectiveness and efficiency of our algorithm.