Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization

Abstract

Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning accuracy, and nonmatching and mismatching are prone to occur, especially when the initial position error is large. To solve this problem, a new algorithm combining the artificial bee colony (ABC) and particle swarm optimization (PSO) was proposed according to the principle of terrain matching, to improve the matching effect. Considering that PSO easily falls into a local optimum, the acceleration factor and inertia weight of PSO were improved. The improved PSO was called WAPSO. ABC was introduced based on WAPSO and could help WAPSO escape local optimum. The final algorithm was termed ABC search-based WAPSO (F-WAPSO). During the continuous iteration of particles, F-WAPSO seeks the optimal position for the particles. Simulation tests show that F-WAPSO can effectively improve the matching accuracy. When the initial position error is 1000 m, the matching error can be reduced to 93.5 m, with a matching time of only 13.7 s.