Robust Variational Auto-Encoder for Radar HRRP Target Recognition

被引：0
|
作者：
Zhai Y. [1 ]
Chen B. [2 ]
机构：
[1] Xi'an Research Institute of Navigation Technology, Xi'an, 710068, Shaanxi
[2] National Laboratory of Radar Signal Processing, Xidian University, Xi'an, 710071, Shaanxi
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 06期
关键词：
Feature extraction; High-resolution range profile (HRRP); Radar automatic target recognition (RATR); Robust variational auto-encoder (RVAE);
D O I：
10.3969/j.issn.0372-2112.2020.06.015
中图分类号：
学科分类号：
摘要：
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. © 2020, Chinese Institute of Electronics. All right reserved.
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页码：1149 / 1155
页数：6
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