Systems Engineering and Electronics ›› 2026, Vol. 48 ›› Issue (5): 1451-1464.doi: 10.12305/j.issn.1001-506X.2026.05.01

• Electronic Technology • Previous Articles     Next Articles

Detection and reconstruction method of fast-moving targets based on deep-water MBES

Yuan WANG1,2(), Xiaodong LIU1,3,4,*(), Shuwen WANG1,3   

  1. 1. Laboratory of Ocean Acoustic Technology,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190,China
    2. School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China
    3. Beijing Engineering Technology Research Center of Ocean Acoustic Equipment,Beijing 100190,China
    4. State Key Laboratory of Acoustics and Marine Information,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2025-03-04 Online:2026-05-27 Published:2026-05-27
  • Contact: Xiaodong LIU E-mail:wangyuan18@mails.ucas.ac.cn;liuxd@mail.ioa.ac.cn

Abstract:

A detection and reconstruction method based on deep-water multibeam echosounder (MBES) is proposed to address the challenge of rapidly real-time imaging fast-changing underwater targets, such as gas leaks or the shoal of fish, during navigation. The method utilizes the multi-sector emission feature of deep-water MBES to enhance the real-time imaging capability of water column. Firstly, real-time detection is achieved by repositioning the three-dimensional scattering points in the target area through angle calculations. Then, the targets point cloud is reconstructed using the Otsu threshold segmentation algorithm, improved based on water body backscattering intensity, and a density-based spatial clustering of applications with noise optimized with the Newton-Raphson-based optimizer, which leads to clearer underwater target images. The proposed method is validated through simulation experiments, and clear underwater gas column image is obtained by processing sea trial data, verifying the validity of the proposed algorithm.

Key words: deep-water multibeam echosounder (MBES), three-dimensional water body, three-dimensional point cloud reconstruction, Newton-Raphson-based optimizer-density-based spatial clustering of applications with noise

CLC Number: 

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