An Improved GPSO-PF for Underwater Terrain-Aided Navigation

In recent years, terrain-aided navigation (TAN) has been rapidly developed for the localization of autonomous underwater vehicles (AUVs). TAN provides a position estimate for vehicles relative to a priori topographic map and is typically a nonlinear filtering model. Particle filtering has attracted considerable attention for addressing nonlinear and non-Gaussian challenges. However, it has problems with particle degradation and particle impoverishment. To tackle the problem in this study, the particle swarm optimization (PSO) optimization approach is employed to increase particle variety while driving particles towards the high probability zone through iterative updates. In addition, prior information is introduced to the particle fitness to reduce the interference of pseudo-peaks and increase the estimate precision. Finally, simulation tests are implemented to verify the effectiveness of the proposed method. Simulation results indicate that the improved GPSO-PF method performs better in the case of likelihood function with multiple peaks.