Topic
Invariant extended Kalman filter
About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.
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TL;DR: In this article, the authors compare several estimation methods for nonlinear stochastic differential equations with discrete time measurements, and show that the likelihood function is computed by Monte Carlo simulations of the transition probability (simulated maximum likelihood SML) using kernel density estimators and functional integrals.
Abstract: This article compares several estimation methods for nonlinear stochastic differential equations with discrete time measurements. The likelihood function is computed by Monte Carlo simulations of the transition probability (simulated maximum likelihood SML) using kernel density estimators and functional integrals and by using the extended Kalman filter (EKF and second-order nonlinear filter SNF). The relation with a local linearization method is discussed. A simulation study for a diffusion process in a double well potential (Ginzburg–Landau equation) shows that, for large sampling intervals, the SML methods lead to better estimation results than the likelihood approach via EKF and SNF. A second study using a nonlinear diffusion coefficient (generalized Cox–Ingersoll–Ross model) demonstrates that the EKF type estimators may serve as efficient alternatives to simple maximum quasilikelihood approaches and Monte Carlo methods.
92 citations
TL;DR: It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.
92 citations
TL;DR: In this article, a general multiple-level quantized innovation Kalman filter (MLQ-KF) was proposed for estimation of linear dynamic stochastic systems, and the optimal filter was given in terms of a simple Riccati difference equation.
92 citations
TL;DR: In this paper, a recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly, and the statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty.
91 citations
01 Aug 1993
TL;DR: Extended Kalman filters - standard, modified and ideal, M.J. Moorman robust adaptive Kalman filtering, A.R. Moghaddamjoo and R.E. Bullock bias in extended Kal man filters - a mathematical analysis.
Abstract: Extended Kalman filters - standard, modified and ideal, M.J. Moorman and T.E. Bullock bias in extended Kalman filters - a mathematical analysis, T.E. Bullock and M.J. Moorman robust adaptive Kalman filtering, A.R. Moghaddamjoo and R.L. Kirlin on-line estimation of signal and noise parameters and adaptive Kalman filtering, P.J. Wojcik adaptive Kalman filtering under irregular environment, G. Chen fisher initialization in the presence of ill-conditioned measurements, D. Catlin initializing the Kalman filter with incompletely specified initial conditions, A. Maravall and V. Gomez set-valued Kalman filtering, D. Morrell and W.C. Stirling distributed filtering using set models for systems with non-Gaussian noise, L. Hong suboptimal Kalman filtering for linear systems with non-Gaussian noises, H.Y. Wu and G. Chen robust stability analysis of Kalman filter under parametric and noise uncertainties, B.S. Chen numerical approximations and other structural issues in practical implementations of Kalman filtering, T.H. Kerr.
91 citations