Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection
文献类型：期刊论文
| 作者 | Yang, Yipu1,2; Yang, Fan2 ; Sun, Liguo1; Xiang, Ti1; Lv, Pin1
|
| 刊名 | IEEE SENSORS JOURNAL
 |
| 出版日期 | 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. |
| WOS关键词 | TARGET ; CLASSIFICATION |
| 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研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000974500000064 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China for Intelligent Robotics Special Project ; Natural Science Foundation of Hebei Province, China |
| 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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300401, 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. |
入库方式： OAI收割
来源：自动化研究所
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