An inertial constraint-aided geomagnetic matching algorithm for underwater navigation

Geomagnetic navigation, known for its passive nature, all-weather operability, and global coverage, offers significant potential for underwater autonomous navigation systems, with an effective geomagnetic matching algorithm being crucial to its success. Based on geomagnetic scalar features, the iterative closest contour point (ICCP) algorithm is widely used. However, its accuracy can be seriously degraded under conditions including multiple contours within the matching area or when the trajectory aligns parallel or perpendicular to the contours. To address these limitations, this paper proposes an inertial constraint based on the Hidden Markov Model (HMM) to improve the ICCP algorithm (HMM-ICCP). This innovative approach introduces a trusted point set selection criterion and employs the HMM to encode the correlation mapping between the inertial navigation system (INS) sequence and the trusted point set. Subsequently, the optimal sequence of closest contour points is determined via the principle of maximum probability, effectively enhancing matching accuracy. Simulation results indicate that, compared to the ICCP algorithm, the HMM-ICCP algorithm achieves accuracy improvements of 89.7%, 51.2%, and 97.6% under specific conditions. In addition, the HMM-ICCP algorithm demonstrates greater robustness to noise and reduces the elapsed time by minimizing the number of iterations.