On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Correspondence: Comments on "Performance evaluation of UKF-based nonlinear filtering"
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system
Automatica (Journal of IFAC)
Brief paper: Adaptive divided difference filtering for simultaneous state and parameter estimation
Automatica (Journal of IFAC)
Brief paper: Derivative-free estimation methods: New results and performance analysis
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
A Numerical-Integration Perspective on Gaussian Filters
IEEE Transactions on Signal Processing
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
Performance evaluation of UKF-based nonlinear filtering
Automatica (Journal of IFAC)
Gaussian filter for nonlinear systems with one-step randomly delayed measurements
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
This paper proposes a Gaussian approximation recursive filter (GASF) for a class of nonlinear stochastic systems in the case that the process and measurement noises are correlated with each other. Through presenting the Gaussian approximations about the two-step state posterior predictive probability density function (PDF) and the one-step measurement posterior predictive PDF, a general GASF framework in the minimum mean square error (MMSE) sense is derived. Based on the framework, the GASF implementation is transformed into computing the multi-dimensional integrals, which is solved by developing a new divided difference filter (DDF) with correlated noises. Simulation results demonstrate the superior performance of the proposed DDF as compared to the standard DDF, the existing UKF and EKF with correlated noises.