Deep Learning for Radar

被引:0
作者
Mason, Eric [1 ]
Yonel, Bariscan [1 ]
Yazici, Birsen [1 ]
机构
[1] Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, 110 8th St, Troy, NY 12180 USA
来源
2017 IEEE RADAR CONFERENCE (RADARCONF) | 2017年
基金
美国国家科学基金会;
关键词
D O I
暂无
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
Motivated by the recent advances in deep learning, we lay out a vision of how deep learning techniques can be used in radar. Specifically, our discussion focuses on the use of deep learning to advance the state-of-the-art in radar imaging. While deep learning can be directly applied to automatic target recognition (ATR), the relevance of these techniques in other radar problems is not obvious. We argue that deep learning can play a central role in advancing the state-of-the-art in a wide range of radar imaging problems, discuss the challenges associated with applying these methods, and the potential advancements that are expected. We lay out an approach to design a network architecture based on the specific structure of the synthetic aperture radar (SAR) imaging problem that augments learning with traditional SAR modelling. This framework allows for capture of the non-linearity of the SAR forward model. Furthermore, we demonstrate how this process can be used to learn and compensate for trajectory based phase error for the autofocus problem.
引用
收藏
页码:1703 / 1708
页数:6
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