Volume 14 Issue 2
Apr.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(2): 249-268
CHEN Xiaolong, RAO Guilin, GUAN Jian, et al. Passive radar low slow small detection dataset (LSS-PR-1.0) and multi-domain feature extraction and analysis methods[J]. Journal of Radars, 2025, 14(2): 249–268. doi: 10.12000/JR24145
Citation: CHEN Xiaolong, RAO Guilin, GUAN Jian, et al. Passive radar low slow small detection dataset (LSS-PR-1.0) and multi-domain feature extraction and analysis methods[J]. Journal of Radars, 2025, 14(2): 249–268. doi: 10.12000/JR24145
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Passive Radar Low Slow Small Detection Dataset (LSS-PR-1.0) and Multi-domain Feature Extraction and Analysis Methods

DOI: 10.12000/JR24145 CSTR: 32380.14.JR24145
  • 1.

    Naval Aviation University, Yantai 264001, China

  • 2.

    School of Electronic Information, Wuhan University, Wuhan 430072, China

Funds:  The National Natural Science Foundation of China (62222120, 61931021, 61931015), Shandong Provincial Natural Science Foundation (ZR2024JQ003)
More Information
  • Corresponding author: CHEN Xiaolong, cxlcxl1209@163.com; YI Jianxin, jxyi@whu.edu.cn
  • Received Date: 2024-07-17
  • Rev Recd Date: 2024-10-17
    • Available Online: 2024-10-18
  • Publish Date: 2024-11-05
    Abstract
  • Passive radar plays an important role in early warning detection and Low Slow Small (LSS) target detection. Due to the uncontrollable source of passive radar signal radiations, target characteristics are more complex, which makes target detection and identification extremely difficult. In this paper, a passive radar LSS detection dataset (LSS-PR-1.0) is constructed, which contains the radar echo signals of four typical sea and air targets, namely helicopters, unmanned aerial vehicles, speedboats, and passenger ships, as well as sea clutter data at low and high sea states. It provides data support for radar research. In terms of target feature extraction and analysis, the singular-value-decomposition sea-clutter-suppression method is first adopted to remove the influence of the strong Bragg peak of sea clutter on target echo. On this basis, four categories of ten multi-domain feature extraction and analysis methods are proposed, including time-domain features (relative average amplitude), frequency-domain features (spectral features, Doppler waterfall plot, and range Doppler features), time-frequency-domain features, and motion features (heading difference, trajectory parameters, speed variation interval, speed variation coefficient, and acceleration). Based on the actual measurement data, a comparative analysis is conducted on the characteristics of four types of sea and air targets, summarizing the patterns of various target characteristics and laying the foundation for subsequent target recognition.

     

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