Submit Manuscript
Peer Review
Editor Work
| Citation: | CHEN Xiaolong, Yuan Wang, Du Xiaolin, et al. Multiband FMCW radar LSS-target detection dataset (LSS-FMCWR-1.0) and high-resolution micromotion feature extraction method[J]. Journal of Radars, 2024, 13(3): 539–553. doi: 10.12000/JR23142
|
Multiband FMCW Radar LSS-target Detection Dataset (LSS-FMCWR-1.0) and High-resolution Micromotion Feature Extraction Method
DOI: 10.12000/JR23142
More Information-
Abstract
Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities. -
-
References
[1] 陈小龙, 南钊, 张海, 等. 飞鸟与旋翼无人机雷达微多普勒测量实验研究[J]. 电波科学学报, 2021, 36(5): 704–714. doi: 10.12265/j.cjors.2020192.CHEN Xiaolong, NAN Zhao, ZHANG Hai, et al. Experimental research on radar micro-Doppler of flying bird and rotor UAV[J]. Chinese Journal of Radio Science, 2021, 36(5): 704–714. doi: 10.12265/j.cjors.2020192.[2] 陈小龙, 陈唯实, 饶云华, 等. 飞鸟与无人机目标雷达探测与识别技术进展与展望[J]. 雷达学报, 2020, 9(5): 803–827. doi: 10.12000/JR20068.CHEN Xiaolong, CHEN Weishi, RAO Yunhua, et al. Progress and prospects of radar target detection and recognition technology for flying birds and unmanned aerial vehicles[J]. Journal of Radars, 2020, 9(5): 803–827. doi: 10.12000/JR20068.[3] 何炜琨, 孙景波, 王晓亮, 等. 基于RSP-CFD方法的小型旋翼无人机微动特征提取[J]. 信号处理, 2021, 37(3): 399–408. doi: 10.16798/j.issn.1003-0530.2021.03.010.HE Weikun, SUN Jingbo, WANG Xiaoliang, et al. Micro-motion feature extraction of micro-rotor UAV based on RSP-CFD method[J]. Journal of Signal Processing, 2021, 37(3): 399–408. doi: 10.16798/j.issn.1003-0530.2021.03.010.[4] 宋晨, 周良将, 吴一戎, 等. 基于时频集中度指标的多旋翼无人机微动特征参数估计方法[J]. 电子与信息学报, 2020, 42(8): 2029–2036. doi: 10.11999/JEIT190309.SONG Chen, ZHOU Liangjiang, WU Yirong, et al. An estimation method of micro-movement parameters of UAV based on the concentration of time-frequency[J]. Journal of Electronics & Information Technology, 2020, 42(8): 2029–2036. doi: 10.11999/JEIT190309.[5] SONG Chen, WU Yirong, ZHOU Liangjiang, et al. A multicomponent micro-Doppler signal decomposition and parameter estimation method for target recognition[J]. Science China Information Sciences, 2019, 62(2): 29304. doi: 10.1007.s11432-0180-9491-y.[6] 马娇, 董勇伟, 李原, 等. 多旋翼无人机微多普勒特性分析与特征提取[J]. 中国科学院大学学报, 2019, 36(2): 235–243. doi: 10.7523/j.issn.2095-6134.2019.02.011.MA Jiao, DONG Yongwei, LI Yuan, et al. Multi-rotor UAV’s micro-Doppler characteristic analysis and feature extraction[J]. Journal of University of Chinese Academy of Sciences, 2019, 36(2): 235–243. doi: 10.7523/j.issn.2095-6134.2019.02.011.[7] HE Weikun, SUN Jingbo, ZHANG Xinyun, et al. Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR[J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1127–1139. doi: 10.23919/JSEE.2022.000138.[8] YANG Degui, LI Jin, LIANG Buge, et al. A multi-rotor drone micro-motion parameter estimation method based on CVMD and SVD[J]. Remote Sensing, 2022, 14(14): 3326. doi: 10.3390/rs14143326.[9] KANG K B, CHOI J H, CHO B L, et al. Analysis of micro-Doppler signatures of small UAVs based on Doppler spectrum[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(5): 3252–3267. doi: 10.1109/TAES.2021.3074208.[10] AUGER F and FLANDRIN P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1068–1089. doi: 10.1109/78.382394.[11] LI Zhen, GAO Jinghuai, LI Hui, et al. Synchroextracting transform: The theory analysis and comparisons with the synchrosqueezing transform[J]. Signal Processing, 2020, 166: 107243. doi: 10.1016/j.sigpro.2019.107243.[12] YU Gang, YU Mingjin, and XU Chuanyan. Synchroextracting transform[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8042–8054. doi: 10.1109/TIE.2017.2696503.[13] 章鹏飞, 李刚, 霍超颖, 等. 基于双雷达微动特征融合的无人机分类识别[J]. 雷达学报, 2018, 7(5): 557–564. doi: 10.12000/JR18061.ZHANG Pengfei, LI Gang, HUO Chaoying, et al. Classification of drones based on micro-Doppler radar signatures using dual radar sensors[J]. Journal of Radars, 2018, 7(5): 557–564. doi: 10.12000/JR18061.[14] PARK J, JUNG D H, BAE K B, et al. Range-Doppler map improvement in FMCW radar for small moving drone detection using the stationary point concentration technique[J]. IEEE Transactions on Microwave Theory and Techniques, 2020, 68(5): 1858–1871. doi: 10.1109/TMTT.2019.2961911.[15] 杜兰, 陈晓阳, 石钰, 等. MMRGait-1.0: 多视角多穿着条件下的雷达时频谱图步态识别数据集[J]. 雷达学报, 2023, 12(4): 892–905. doi: 10.12000/JR22227.DU Lan, CHEN Xiaoyang, SHI Yu, et al. MMRGait-1.0: A radar time-frequency spectrogram dataset for gait recognition under multi-view and multi-wearing conditions[J]. Journal of Radars, 2023, 12(4): 892–905. doi: 10.12000/JR22227.[16] 金添, 宋永坤, 戴永鹏, 等. UWB-HA4D-1.0: 超宽带雷达人体动作四维成像数据集[J]. 雷达学报, 2022, 11(1): 27–39. doi: 10.12000/JR22008.JIN Tian, SONG Yongkun, DAI Yongpeng, et al. UWB-HA4D-1.0: An ultra-wideband radar human activity 4D imaging dataset[J]. Journal of Radars, 2022, 11(1): 27–39. doi: 10.12000/JR22008.[17] LEONARDI M, LIGRESTI G, PIRACCI E, et al. Drones classification by the use of a multifunctional radar and micro-Doppler analysis[J]. Drones, 2022, 6(5): 124. doi: 10.3390/drones6050124.[18] RAVAL D, HUNTER E, HUDSON S, et al. Convolutional neural networks for classification of drones using radars[J]. Drones, 2021, 5(4): 149. doi: 10.3390/drones 5040149. -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. Component of the FMCW radar
- Figure 2. Ku-band and L-band FMCW radar
- Figure 3. Diagram of the transmitted and received signals of the triangular wave modulated FMCW radar
- Figure 4. Echo signal processing process
- Figure 5. Time spectrum and range Doppler of various types of unmanned aerial vehicles
- Figure 6. Time spectrum diagram of fixed wing UAV
- Figure 7. UAV acquisition scenarios and types of UAVs
- Figure 8. Schematic diagram of specific dataset structure
- Figure 9. Algorithm flow chart
- Figure 10. Echo data preprocessing process
- Figure 11. Data before and after removing jitter interference (LSS-FMCWR-1.0:06-2023.5.8-0.3-100-9-K (1).mat)
- Figure 12. Renyi entropy values for different time-frequency methods
- Figure 13. Under different SNRs, the Renyi entropies of the different time-frequency analysis methods
- Figure 14. STFT and LSET of Wu 2 UAV signal (LSS-FMCWR-1.0: 04-2023.5.8-0.3-100-13-L (18).mat)
- Figure 15. Time domain signal of Wu 2 UAV (04)
- Figure 16.
- Figure 17. Release webpage of LSS-FMCWR-1.0: Multiband FMCW radar LSS-target echo dataset