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Articles | Volume XLVI-3/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3/W1-2022, 149–154, 2022
https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-149-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-3/W1-2022, 149–154, 2022
https://doi.org/10.5194/isprs-archives-XLVI-3-W1-2022-149-2022
 
22 Apr 2022
22 Apr 2022

PARTICLE SWARM OPTIMIZATION FOR UNDERWATER GRAVITY-MATCHING: APPLICATIONS IN NAVIGATION AND SIMULATION

M. OuYang1,2,3, H. Fan1, and P. Du2,3 M. OuYang et al.
  • 1Institute of Geospatial Information, Information Engineering University, Zhengzhou, 450001, China
  • 2Xi’an Research Institute of Surveying and Mapping, Xi’an,710054, China
  • 3Stake Key Laboratory of Geo-information Engineering, Xi’an, 710054, China

Keywords: Gravity matching navigation, Particle swarm optimization, Affine transformation

Abstract. This paper developed a two-stage solution for underwater gravity matching navigation based on the particle swarm optimization algorithm and affine transformation. The first stage established a starting point, and the second stage treated the matching track gained through affine transformation as a particle at the same starting point, followed by the application of the particle swarm optimization algorithm to obtain the optimal solution. To avoid falling into a local optimal solution, a convergence factor was incorporated into the optimization process in addition to the linear decreasing weight. This was followed by the addition of a constraint on the velocity and position of the particles, which was then updated in an iterating process. Two simulated navigation tracks were employed for experiments. The results revealed that the algorithm was capable of matching actual tracks in real time. Additionally, the results were found to be consistent with those obtained from the real-world tracks, with all the locations and gravity anomaly deviations falling within a tolerable range. However, when there were too many matching track points, the algorithm efficiency declined in terms of calculation time. This entails improving the algorithm through the segmentation technique.