An Approach to Seabed Terrain Matching Utilizing Hybrid Particle Swarm Optimization

Abstract

When most of terrain matching algorithms are directly applied to seabed terrain-aided navigation system (STAN), the positioning accuracy declines sharply and the algorithm become unstable because of the particularity of seabed terrain. A new approach of seabed terrain matching algorithm is proposed in this paper. Unlike traditional terrain matching approaches, the strategy based on particle swarm optimization (PSO) is used in the proposed algorithm instead of traversal search, and the mean Hausdorff distance is used as similarity measure for its super anti-interference and fault-tolerance performance. Furthermore, a hybrid PSO algorithm combined with chaotic search is proposed in application to improve the local exploitation quality. The experimental results based on electronic chart evaluate the algorithm’s great superiority, the number of computation and positioning error are reduced greatly.