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CHEN Xiaolong, YUAN Wang, DU Xiaolin, et al. Multi-band multi-angle FMCW radar low-slow-small target detection dataset (LSS-FMCWR-2.0) and feature fusion classification methods[J]. Journal of Radars, in press. doi: 10.12000/JR25004
Citation: CHEN Xiaolong, YUAN Wang, DU Xiaolin, et al. Multi-band multi-angle FMCW radar low-slow-small target detection dataset (LSS-FMCWR-2.0) and feature fusion classification methods[J]. Journal of Radars, in press. doi: 10.12000/JR25004
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Multi-band Multi-angle FMCW Radar Low-Slow-Small Target Detection Dataset (LSS-FMCWR-2.0) and Feature Fusion Classification Methods

DOI: 10.12000/JR25004 CSTR: 32380.14.JR25004
  • 1.

    Naval Aviation University, Yantai 264001, China

  • 2.

    YanTai University, Yantai 264005, China

Funds:  The National Natural Science Foundation of China (62222120), National Key Research and Development Program of China (2024YFB3909804), Shandong Natural Science Foundation (ZR2024JQ003)
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    Abstract
  • This study addresses the issue of fine-grained feature extraction and classification for Low-Slow-Small (LSS) targets, such as birds and drones, by proposing a multi-band multi-angle feature fusion classification method. First, data from five types of rotorcraft drones and bird models were collected at multiple angles using K-band and L-band frequency-modulated continuous-wave radars, forming a dataset for LSS target detection. Second, to capture the periodic vibration characteristics of the L-band target signals, empirical mode decomposition was applied to extract high-frequency features and reduce noise interference. For the K-band echo signals, short-time Fourier transform was applied to obtain high-resolution micro-Doppler features from various angles. Based on these features, a Multi-band Multi-angle Feature Fusion Network (MMFFNet) was designed, incorporating an improved convolutional long short-term memory network for temporal feature extraction, along with an attention fusion module and a multiscale feature fusion module. The proposed architecture improves target classification accuracy by integrating features from both bands and angles. Validation using a real-world dataset showed that compared with methods relying on single radar features, the proposed approach improved the classification accuracy for seven types of LSS targets by 3.1% under a high Signal-to-Noise Ratio (SNR) of 5 dB and by 12.3% under a low SNR of −3 dB.

     

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