Multi-View HRRP Recognition Based on Denoising Features Enhancement

被引：1
|
作者：
Fan, Lei [1 ]
Yang, Qi [1 ]
Zeng, Yang [1 ]
Deng, Bin [1 ]
Wang, Hongqiang [1 ]
机构：
[1] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha, Peoples R China
来源：
2021 13TH GLOBAL SYMPOSIUM ON MILLIMETER-WAVES & TERAHERTZ (GSMM) | 2021年
基金：
中国国家自然科学基金;
关键词：
multi-view HRRP; RATR; deep learning; signal denoising;
D O I：
10.1109/GSMM53250.2021.9511967
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
High resolution range profiles (HRRPs) have always been an important hot spot in radar automatic target recognition (RATR). However, the problem regarding enhancement in data reliability of HRRPs to improve the final recognition accuracy remains challenging. Recent developments in denoising features enhancement and multi-view data processing provide a potential solution to the problem. In this paper, a multi-view HRRP recognition method based on denoising features enhancement is proposed. A denoising network is first proposed to suppress clutter and enhance the ability of features extraction. Then, multi-view data processing is applied to improve the recognition accuracy rate and data reliability. The effectiveness in terms of signal denoising and the improvement in terms of the recognition accuracy rate are validated by simulation.
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收藏
页数：3
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