Adaptive fuzzy neural network-aided progressive Gaussian approximate filter for GPS/INS integration navigation. (15th August 2022)

• Record Type: Journal Article
• Authors: Lai, Xin; Tong, Siqi; Zhu, Guorui
• Is Part Of: Measurement. Volume 200(2022)
• Publication Date: 2022-08-15
• Subjects: Nonlinear filter -- Progressive measurement update -- Neural network -- Adaptive fuzzy control; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111641
• Languages: English
• ISSNs: 0263-2241

Abstract

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. Highlights: An adaptive fuzzy neural network-aided PGAF with variable step size is proposed. Measurement noise covariance matrix (MNCM) is adjusted by adaptive fuzzy control. State estimation error covariance matrix is optimized using neural network. Using variational Bayesian method jointly estimate the state vector, MNCM and step size. This method is robust for the nonlinear filtering problem with large priori uncertainty.