Correlation research between deep features of HRRP sparse auto-encoder and scattering center features

被引：0
|
作者：
Huo C. [1 ]
Yan H. [1 ]
Feng X. [1 ]
Yin H. [1 ]
Xing X. [1 ]
Lu J. [1 ]
机构：
[1] Science and Technology on Electromagnetic Scattering Laboratory, Beijing Institute of Environmental Features, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 11期
关键词：
Deep feature; High resolution range profile (HRRP); Scattering center feature; Sparse auto-encoder;
D O I：
10.12305/j.issn.1001-506X.2021.11.02
中图分类号：
学科分类号：
摘要：
A sparse auto-encoder network is used to learn and train one-dimensional high resolution range profile (HRRP) of typical targets. A comprehensive weight coefficient is defined based on the weight coefficient matrix of each layer. By comparing the weight coefficient and dimension reduction feature with the scattering center feature, it is found that there is a certain correlation between the deep feature of sparse auto-encoder and the scattering center feature. And the physical meaning of the comprehensive weight coefficient and deep dimension reduction feature is explained in this paper. Firstly, a sparse auto-encoder network is constructed for HRRP. After deep learning, the weight coefficient after training and the feature after dimension reduction are obtained, and the correlation with the position feature and intensity distribution feature of the scattering center is studied. The results show that the comprehensive weight coefficient matrix is a dictionary-like coefficient matrix closely related to the scattering center, which reflects the possible molecule set of the strong scattering center position changing with the angle in the range domain; and the dimension reduction feature can realize the learning and extraction of the strong scattering center, which reflects the change of the strong scattering center position and intensity with the angle. Finally, the influence of the number of training layers and the dimension reduction dimension on the learning and training results is analyzed, which can guide the selection of the subsequent network parameters. For the first time, this paper studies the interpretability of deep learning features for radar HRRP data, which provides a useful guidance for the subsequent extensive application of deep learning in radar data processing. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：3040 / 3053
页数：13
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