An Algorithm for Sound Velocity Error Correction Using GA-SVR Considering the Distortion Characteristics of Seabed Topography Measured by Multibeam Sonar Mounted on Autonomous Underwater Vehicle

Abstract

Aiming to address the problem that autonomous underwater vehicle (AUV) with multibeam echo sounder system (MBES) for underwater topographic survey cannot autonomously obtain the sound velocity of the acoustic path to correct bathymetric data, this article proposes a framework to correct sound velocity error (SVE) for AUV with MBES, which include sound velocity profile (SSP) extension and SVE correction using genetic algorithm (GA) and support vector regression (SVR). The SSP is first calculated by extending the temperature and conductivity profiles to the seafloor based on the trend of temperature and conductivity with depth, which is obtained by using the least squares method to fit the data of conductivity temperature depth sensor carried by AUV. Then, the SSP is calculated according to the empirical sound speed formula. To eliminate the SVE in the extended SSP, the function of water depth, lateral distance, and SVE was first derived. Then, the SVE of the main survey line was corrected, including calculating the discrepancy values between the matching point pairs of the main and auxiliary survey lines, and establishing a regression model about depth error using the SVR algorithm. Finally, the GA algorithm was used to optimize the hyperparameters of the SVR algorithm. The test results demonstrate that the proposed method effectively reduces the impact of SVE on multibeam bathymetric data in both middle–deep and shallow water areas. The algorithm is not limited by terrain complexity, providing a solution for the lack of SSP in AUV autonomous topographic survey.

Keywords

• Autonomous underwater vehicle (AUV)
• genetic algorithm (GA)
• multibeam echo sounder system (MBES)
• sound velocity error (SVE)
• support vector regression (SVR)