A Geomagnetic Matching Navigation Algorithm Based on VDDTW-AICCP

Geomagnetic navigation is an important method for underwater vehicles to navigate accurately. In the geomagnetic matching navigation system, the matching algorithm directly affects the navigation performance. The traditional Iterated Closest Contour Point (ICCP) algorithm performs well when the initial error is small. However, it is prone to falling into local optimum and leading to matching failure under large initial error conditions. To address this issue, this paper proposes a combined matching method based on the Vector Derivative Dynamic Time Warping (VDDTW) algorithm and the Affine Iterated Closest Contour Point (AICCP) algorithm. This method first employs the VDDTW algorithm to perform coarse matching of the geomagnetic sequences, thereby reducing the range of initial errors. Subsequently, high-precision iterative corrections are conducted through the AICCP algorithm, ultimately realizing reliable matching with enhanced positional accuracy. The method significantly improves geomagnetic matching accuracy under large initial errors through the integration of global and local search strategies. The simulation results show that compared with the traditional ICCP geomagnetic matching algorithm, the proposed approach reduces average positioning errors by 55.6%. When compared with the Mean Square Difference-Iterated Closest Contour Point (MSD-ICCP) algorithm, the average positioning error is reduced by 66.1%. The results validate the algorithm's capability to maintain high positioning accuracy in complex geomagnetic environments, providing a viable technical pathway for autonomous navigation of submersible vehicles.