A calibration method of DVL in integrated navigation system based on particle swarm optimization

Abstract

In the underwater SINS/DVL integrated navigation system, the calibration problem between SINS and DVL sensors is one of the critical factors affecting the accuracy of integrated navigation. Traditional calibration methods usually require long-term filter estimation or require the vehicle to operate specific maneuvers, which have limitations. In this paper, a DVL calibration algorithm using a particle swarm optimization (PSO) algorithm is proposed. The SINS/GPS integrated navigation velocity and DVL output velocity are constructed into two point sets, thereby converting the calibration problem into a Wahba problem and using the particle swarm optimization algorithm to solve the optimal rotation matrix. Simulation experiments and semi-physical experiments show that the proposed calibration method can quickly and accurately estimate the scale factor and installation misalignment angle between SINS and DVL sensors, and the integrated navigation accuracy after calibration is significantly improved. Compared with the conventional calibration approaches, the calibration performance of the proposed method is better.