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

doi:10.1109/JSEN.2023.3254525

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统认知与决策实验室

	Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection
	Yang, Yipu 1,2; Yang, Fan 1; Sun, Liguo 2; Xiang, Ti 2; Lv, Pin 2
刊名	IEEE SENSORS JOURNAL
	2023-04-15
卷号	23 期号:8 页码:8639-8653
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
ISSN号	1530-437X
DOI	10.1109/JSEN.2023.3254525
通讯作者	Yang, Fan(yf_hebut@sina.com) ; Lv, Pin(pin.lv@ia.ac.cn)
英文摘要	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.
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]
WOS关键词	TARGET ; CLASSIFICATION
WOS研究方向	Engineering ; Instruments & Instrumentation ; Physics
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000974500000064
资助机构	National Key Research and Development Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53266]
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
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.