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| 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
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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
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Zhou Yu, Wang Hai-peng, Chen Si-zhe. SAR Automatic Target Recognition Based on Numerical Scattering Simulation and Model-based Matching[J]. Journal of Radars, 2015, 4(6): 666-673. doi: 10.12000/JR15080
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- 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