Forward-looking Scanning Radar Angular Superresolution Based on Modified Scale Recurrent Network

被引：0
|
作者：
Li, Jie [1 ]
Li, Wenchao [1 ]
Peng, Yangyang [1 ]
Wu, Junjie [1 ]
Yang, Jianyu [1 ]
机构：
[1] Univ Elect Sci & Technol China, Chengdu 611731, Sichuan, Peoples R China
来源：
2022 IEEE RADAR CONFERENCE (RADARCONF'22) | 2022年
关键词：
Forward-looking Imaging; Scanning radar; Angular superresolution; Neural Network;
D O I：
10.1109/RADARCONF2248738.2022.9764167
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：
Forward-looking scanning radar has important applications in the fields of autonomous landing and precision guidance, but it has the problem of poor angular resolution. In this paper, by replacing the fixed weights in residual block with the learned weights, to realize adaptive fusion of features at different levels, a modified scale recurrent network is proposed and used to improve the angular resolution of forward-looking scanning radar. Simulations are provided to verify the effectiveness of the proposed network.
引用
收藏
页数：5
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