Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles

Abstract

Underwater terrain-matching navigation technologies have become a popular topic for the high-precision positioning and navigation of autonomous underwater vehicles. This paper proposes an underwater terrain-matching positioning method based on a Markov random field model, which is based on real-time terrain data obtained using a multi-beam echo sounder. It focuses on the strong correlation between adjacent terrain data, which can improve terrain adaptability and matching accuracy. Playback simulation tests were conducted based on actual sea trial data, and the results showed that the proposed method has good positioning performance, which can correct the cumulative errors of inertial navigation systems. The results demonstrated the usability of the proposed method for positioning correction in underwater engineering applications.

Underwater Terrain Matching Method Based on Pulse-Coupled Neural Network for Unmanned Underwater Vehicles

“…The current terrain matching algorithm assumes that the discrepancy in error among all sounding data has the same impact on the terrain matching positioning, disregarding the variation in individual errors. Consequently, when the terrain attributes are unclear, the difference in error among the population is small, indicating the presence of a phenomenon known as 'error equilibration' [17,18]. The error equilibration concept suggests that inaccuracies resulting from nodes with a reduced measurement precision of marine terrain will have a detrimental effect on matching outcomes, especially in flat terrain zones.…”