Background classification method based on deep learning for intelligent automotive radar target detection

2019 ◽  
Vol 94 ◽  
pp. 524-535 ◽  
Author(s):  
Ningbo Liu ◽  
Yanan Xu ◽  
Yonghua Tian ◽  
Hongwei Ma ◽  
Shuliang Wen
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Classification Method ◽  
Radar Target ◽  
Automotive Radar

scholarly journals Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 156
Author(s):  
Wen Jiang ◽  
Yihui Ren ◽  
Ying Liu ◽  
Jiaxu Leng
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Target Detection ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Radar Target ◽  
Future Studies ◽  
Learning Techniques ◽  
Feasible Solutions ◽  
Novel Concept

Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing.

scholarly journals An Overview of Parametric Modeling and Methods for Radar Target Detection with Limited Data

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fangzhou Wang ◽  
Pu Wang ◽  
Xin Zhang ◽  
Hongbin Li ◽  
Braham Himed
Keyword(s):  
Target Detection ◽  
Parametric Modeling ◽  
Radar Target ◽  
Limited Data

scholarly journals Information Geometry for Radar Target Detection with Total Jensen–Bregman Divergence

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 256 ◽  
Author(s):  
Xiaoqiang Hua ◽  
Haiyan Fan ◽  
Yongqiang Cheng ◽  
Hongqiang Wang ◽  
Yuliang Qin
Keyword(s):  
Target Detection ◽  
Information Geometry ◽  
Bregman Divergence ◽  
Radar Target
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