Research on multi-beam line survey problem based on geometric analysis and particle swarm optimization

Authors

  • Yunqi Zhu
  • Chen Xie
  • Yufei Wu
  • Chen Xie

DOI:

https://doi.org/10.54097/h9np1034

Keywords:

Multi-beam bathymetry, Geometric analysis, Particle swarm algorithm.

Abstract

Multi-beam bathymetry uses acoustic reflection to measure the depth of seawater. In this paper, through geometric analysis, a model is established to consider the influence of slope, transducer opening angle and distance from the center point of the survey line on the coverage width and overlap rate. In this paper, the particle swarm optimization is also used to determine the optimal survey line. Calculate the depth, coverage width, and overlap rate based on the distance of the survey line from the center point. The direction of the survey line is determined by the angle between the normal direction of the seabed slope and the horizontal plane projection, and the relationship between the coverage width and the seabed depth is studied. The coverage efficiency of straight lines and curves is the same, and the shortest measurement length is the survey line along the short side of the rectangular sea. The optimization model is established by the overlap rate constraint, and the number of measuring lines and the shortest measurement length are calculated. According to the single-beam bathymetry data, the area is delineated, the length and overlap rate of the survey line are calculated, and the length of the entire survey line is accumulated. The shortest survey line length is obtained by constraining the overlap rate, and the total length of the overlap area that exceeds the limit is calculated. Combined with the number of nautical miles in the direction of the sea area, the percentage of the missed sea area is calculated as the percentage of the sea area to be measured.

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References

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Published

22-05-2024

How to Cite

Zhu, Y., Xie, C., Wu, Y., & Xie, C. (2024). Research on multi-beam line survey problem based on geometric analysis and particle swarm optimization. Highlights in Science, Engineering and Technology, 100, 102-110. https://doi.org/10.54097/h9np1034