Volume 14 Issue 3
Jun.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(3): 589-601
PENG Ziqiang, WANG Han, XUE Ruikai, et al. Research on ship feature recognition and tracking method based on long-line array single-photon LiDAR[J]. Journal of Radars, 2025, 14(3): 589–601. doi: 10.12000/JR25003
Citation: PENG Ziqiang, WANG Han, XUE Ruikai, et al. Research on ship feature recognition and tracking method based on long-line array single-photon LiDAR[J]. Journal of Radars, 2025, 14(3): 589–601. doi: 10.12000/JR25003
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Research on Ship Feature Recognition and Tracking Method Based on Long-line Array Single-photon LiDAR

DOI: 10.12000/JR25003 CSTR: 32380.14.JR25003
  • 1.

    Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

  • 2.

    Hefei National Laboratory, Hefei 230094, China

  • 3.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 4.

    Shanghai Research Center for Quantum Sciences, Shanghai 201315, China

Funds:  Innovation Program for Quantum Science and Technology (2021ZD0300304), Foundation of Shanghai Institute of Technical Physics (CX-482, CX-532), The National Natural Science Foundation of China (42241169, 62205361), Shanghai Municipal Science and Technology Major Project (2019SHZDZX01)
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    Abstract
  • In recent years, surface ship target tracking has been an important issue that needs to be solved in autonomous ship navigation. For three-dimensional environmental perception, LiDAR has the characteristics of high resolution and high precision, for three-dimensional environmental perception. By adding one-dimensional scanning, long-line array LiDAR has a larger field of view compared with single point and area array LiDAR, offering unique advantages in environmental perception. Owing to the inconsistency between the characteristics of surface ships and ground target, and the lack of relevant data sets, the current commonly used fitting methods cannot effectively perceive surface target characteristics. In this paper, an efficient target tracking method for ships is proposed based on the characteristics of single-photon point clouds and long-distance target detection. This method is based on the synchronous clustering and denoising of neighboring points; it uses the prior knowledge of the geometric features of ships to fit through the extraction of ship feature points and surfaces, further reducing the influence of noise. Combined with the extended Kalman filter and velocity estimation method, the real-time and stable trajectory tracking of a 600 m target is realized. The root mean square error of tracking is 0.5 m, with a single-frame processing time of 1.02 s, which meets real-time engineering requirements. The proposed method has also been tested in a complex environment and has a good tracking effect for large ships, which is better than the common fitting tracking method. This provides better information for the subsequent autonomous navigation of intelligent ships, and realizes better obstacle avoidance and path planning for ships.

     

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