Abstract:
The selection of step sizes in the progressive Gaussian approximate filter (PGAF) is important, and it is difficult to select optimal values in practical applications. Fu...Show MoreMetadata
Abstract:
The selection of step sizes in the progressive Gaussian approximate filter (PGAF) is important, and it is difficult to select optimal values in practical applications. Furthermore, in the PGAF, significant integral approximation errors are generated by the repeated approximate calculations of the Gaussian weighted integrals, which results in an inaccurate measurement noise covariance matrix (MNCM). To solve these problems, in this paper, the step sizes and the MNCM are jointly estimated based on the variational Bayesian (VB) approach. By incorporating the adaptive estimates of step sizes and the MNCM into the PGAF framework, a novel PGAF with variable step size is proposed. Simulation results illustrate that the proposed filter has higher estimation accuracy than existing state-of-the-art nonlinear Gaussian approximate filters.
Published in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 12-17 May 2019
Date Added to IEEE Xplore: 17 April 2019
ISBN Information: