Abstract
To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.
Highlights
Underwater terrain-aided navigation (UTAN) has the advantages of low cost, long-term, concealed, all-weather, and so on, which is a hotspot in the field of underwater autonomous navigation.[1]
Based on the Bayesian filtering model for UTAN proposed by Chen et al.,[14] the probability density distribution can be approximated by Gaussian function pðÞ, and the Gaussian Particle Filtering (GPF) can be expressed as follows: Measurement update
The process of Radial Basis Function Neural Network (RBFNN)-GSPF method for UTAN is as follows: In this article, a GSPF based on RBFNN is proposed, which use RBFNN to calculate the weights of Gaussian components
Summary
Underwater terrain-aided navigation (UTAN) has the advantages of low cost, long-term, concealed, all-weather, and so on, which is a hotspot in the field of underwater autonomous navigation.[1]. Bergman et al.[6] first proposed the terrain-aided navigation (TAN) model for aircraft under the Bayesian framework and pointed out the problem substance is the nonlinear Bayesian posterior probability estimation. Aiming at this defect, many improved PF methods for TAN have been proposed in recent years.[8,9,10] These methods improve and optimize the resampling process to increase particle diversity. Many improved PF methods for TAN have been proposed in recent years.[8,9,10] These methods improve and optimize the resampling process to increase particle diversity These methods can only postpone the particle depletion, instead of completely eliminated. Simulation results show that the proposed method can improve terrain positioning accuracy effectively has strong robustness to terrain features, which is suitable for autonomous underwater vehicle (AUV) navigation
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