Adaptive fuzzy neural network-aided progressive Gaussian approximate filter for GPS/INS integration navigation

Abstract

To address the difficulty of high uncertainty priori information in nonlinear filtering that occurs in tightly coupled navigation and positioning systems, a novel adaptive fuzzy neural network-aided progressive Gaussian filter is proposed in this paper to further improve the robustness and accuracy of the filter. A joint estimation of the step size and measurement noise covariance matrix is performed by a variational Bayesian approach. The estimation accuracy of the progressive Gaussian approximation filter is thereby enhanced. In particular, the measurement noise covariance matrix is adjusted using an adaptive fuzzy algorithm, and the state estimation error covariance matrix is optimized exploiting a neural network model. The proposed filter is experimentally verified to have higher accuracy than the existing state-of-the-art filters.