Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

被引：30

作者：

Jiang, Wen ^{[1
]}

Wang, Yanping ^{[1
]}

Li, Yang ^{[1
]}

Lin, Yun ^{[1
]}

Shen, Wenjie ^{[1
]}

机构：

[1] North China Univ Technol, Sch Informat Sci & Technol, Radar Monitoring Technol Lab, Beijing 100144, Peoples R China

来源：

REMOTE SENSING | 2023年 / 15卷 / 15期

基金：

北京市自然科学基金;

关键词：

radar automatic target recognition; radar target characteristics; deep learning; artificial intelligence; radar signal processing; RECURRENT ATTENTIONAL NETWORK; HUMAN MOTION RECOGNITION; EXTRACTING POLES; NEURAL-NETWORK; SAR ATR; CLASSIFICATION; MODEL; SYSTEM;

D O I：

10.3390/rs15153742

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.

引用

页数：41

共 167 条

[51]

Haykin S., 2009, Neural networks and learning machines, V3

[52]

He H, 2017, CHIN AUTOM CONGR, P435, DOI 10.1109/CAC.2017.8242807

[53] Reducing the dimensionality of data with neural networks [J].

Hinton, G. E. ;

Salakhutdinov, R. R. .

SCIENCE, 2006, 313 (5786) :504-507

[54] A fast learning algorithm for deep belief nets [J].

Hinton, Geoffrey E. ;

Osindero, Simon ;

Teh, Yee-Whye .

NEURAL COMPUTATION, 2006, 18 (07) :1527-1554

[55]

Hospedales T, 2020, Arxiv, DOI arXiv:2004.05439

[56] FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition [J].

Hou, Xiyue ;

Ao, Wei ;

Song, Qian ;

Lai, Jian ;

Wang, Haipeng ;

Xu, Feng .

SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (04)

[57] GENERALIZED PENCIL-OF-FUNCTION METHOD FOR EXTRACTING POLES OF AN EM SYSTEM FROM ITS TRANSIENT-RESPONSE [J].

HUA, Y ;

SARKAR, TK .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 1989, 37 (02) :229-234

[58] OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation [J].

Huang, Lanqing ;

Liu, Bin ;

Li, Boying ;

Guo, Weiwei ;

Yu, Wenhao ;

Zhang, Zenghui ;

Yu, Wenxian .

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (01) :195-208

[59] What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs [J].

Huang, Zhongling ;

Pan, Zongxu ;

Lei, Bin .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (04) :2324-2336

[60] Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar [J].

Huizing, Albert ;

Heiligers, Matijs ;

Dekker, Bastiaan ;

de Wit, Jacco ;

Cifola, Lorenzo ;

Harmanny, Ronny .

IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE, 2019, 34 (11) :46-55

← 1 2 3 4 5 6 7 8 9 10 →