Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection

doi:10.1109/JSEN.2023.3254525

	Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection
	Yang, Yipu 1,2; Yang, Fan1 ; Sun, Liguo 2; Xiang, Ti 2; Lv, Pin2
发表期刊	IEEE SENSORS JOURNAL
ISSN	1530-437X
	2023-04-15
卷号	23 期号:8 页码:8639-8653
通讯作者	Yang, Fan(yf_hebut@sina.com) ; Lv, Pin(pin.lv@ia.ac.cn)
摘要	While recent years have witnessed an increasing number of commercial applications of unmanned aerial vehicles (UAVs), an imperative problem people have to face is the rapid growth of malicious use. So, it is imperative for security agencies to develop anti-UAV technology. The introduction of deep learning (DL) has a positive influence on radar signal processing, but DL-based methodologies have yet to be widespread in radar target detection because of the lack of unique architecture based on radar echo characteristics and the annotation method of radar data. In this article, a novel Transformer-based architecture is proposed, which transforms the problem of UAV detection into a binary classification task in each range cell. The complex encoder architecture and the Transformer-based extractor are designed to extract the Doppler frequency shift feature and the micro-Doppler signature (mDS) of a UAV simultaneously. The well-designed architecture based on radar echo characteristics can achieve a combination training of echoes with different coherent processing intervals (CPIs). In addition, we provide an annotation method and a data augmentation skill for our real measured dataset. The results of the experiment demonstrate that the proposed method has better detection performance and measuring accuracy under different SNRs in comparison with traditional radar target detection and other DL-based methods.
关键词	Radar Radar detection Clutter Sensors Frequency modulation Task analysis Radar measurements Doppler frequency shift micro-Doppler signature (mDS) radar echo processing Transformer unmanned aerial vehicle (UAV) detection
DOI	10.1109/JSEN.2023.3254525
关键词[WOS]	TARGET ; CLASSIFICATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China for Intelligent Robotics Special Project[2019YFB131202] ; Natural Science Foundation of Hebei Province, China[F2019202364]
项目资助者	National Key Research and Development Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China
WOS研究方向	Engineering ; Instruments & Instrumentation ; Physics
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied
WOS记录号	WOS:000974500000064
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53267
专题	复杂系统认知与决策实验室
通讯作者	Yang, Fan; Lv, Pin
作者单位	1.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300401, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yang, Yipu,Yang, Fan,Sun, Liguo,et al. Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection[J]. IEEE SENSORS JOURNAL,2023,23(8):8639-8653.
APA	Yang, Yipu,Yang, Fan,Sun, Liguo,Xiang, Ti,&Lv, Pin.(2023).Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection.IEEE SENSORS JOURNAL,23(8),8639-8653.
MLA	Yang, Yipu,et al."Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection".IEEE SENSORS JOURNAL 23.8(2023):8639-8653.