Volume 14 Issue 3
Jun.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(3): 548-561
XIAO Zhen, GU Yanfeng, JIANG Yanze, et al. Full-waveform small-footprint LiDAR multi-target echo waveform lightweight detection by spatio-temporal coupling models[J]. Journal of Radars, 2025, 14(3): 548–561. doi: 10.12000/JR24245
Citation: XIAO Zhen, GU Yanfeng, JIANG Yanze, et al. Full-waveform small-footprint LiDAR multi-target echo waveform lightweight detection by spatio-temporal coupling models[J]. Journal of Radars, 2025, 14(3): 548–561. doi: 10.12000/JR24245
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Full-waveform Small-footprint LiDAR Multi-target Echo Waveform Lightweight Detection by Spatio-temporal Coupling Models

DOI: 10.12000/JR24245 CSTR: 32380.14.JR24245
  • 1.

    School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150006, China

  • 2.

    Heilongjiang Key Laboratory of Space and Ground Integrated Intelligent Remote Sensing Technology, Harbin 150006, China

Funds:  The National Natural Science Foundation of China through the Major Scientific Instrument Development Program (62327803), Fund of National Key Laboratory of Laser Spatial Information (LSI2024JCKY06)
More Information
  • Corresponding author: LI Xian, xianli@hit.edu.cn
  • Received Date: 2024-12-10
  • Rev Recd Date: 2025-03-24
    • Available Online: 2025-04-01
  • Publish Date: 2025-04-17
    Abstract
  • Small-footprint full-waveform Light Detection And Ranging (LiDAR) exhibits significant application potential owing to its high penetration capability and ability to capture complete echo data. However, the efficient and accurate processing of massive echo signals remains a crucial challenge for practical use, particularly in advancing waveform decomposition technology. In small-footprint full-waveform LiDAR systems, most echoes are single-target, while only multi-target echoes require detailed decomposition. Current solutions often sacrifice precision by employing simplified rapid waveform decomposition algorithms or process all echoes indiscriminately, resulting in low efficiency and the inability to balance accuracy and speed effectively. This study proposes a spatiotemporal coupling model-driven lightweight algorithm for detecting multi-target echoes in small-footprint full-waveform LiDAR. For the first time, it achieves efficient and accurate detection of multi-target echoes from waveform data with unknown echo counts. The proposed method eliminates redundant computations caused by indiscriminate processing of single-target echoes, significantly reducing waveform decomposition iterations. The technical contributions include constructing a spatiotemporal coupling echo signal model that captures the spatiotemporal characteristics of echo transmission, implementing model-driven lightweight waveform parameter estimation through double Gaussian function superposition fitting, and introducing an adaptive correlation discrimination method based on a signal-to-noise ratio approach. By leveraging the consistency of system-emitted pulses, the proposed method enables lightweight yet accurate multi-target echo detection. Experimental results on terrestrial and airborne waveform datasets demonstrate that our algorithm achieves 98.4% detection accuracy with a 93.1% recall rate. When integrated with four waveform decomposition methods, it improves processing efficiency by 2–3 times. The efficiency gain becomes even more pronounced as the proportion of single-target echoes increases.

     

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