Title :
Approximate non-Gaussian filtering with linear state and observation relations
Author_Institution :
University of Washington, Seattle, Washington, USA
fDate :
2/1/1975 12:00:00 AM
Abstract :
Two approaches to the non-Gaussian filtering problem are presented. The proposed filters retain the computationally attractive recursive structure of the Kalman filter and they approximate well the exact minimum variance filter in cases where either 1) the state noise is Gaussian or its variance small in comparison to the observation noise variance, or 2) the observation noise is Gaussian and the system is one step observable. In both cases, the state estimate is formed as a linear prediction corrected by a nonlinear function of past and present observations. Some simulation results are presented.
Keywords :
Kalman filtering; Linear systems, stochastic discrete-time; Nonlinear filtering; Recursive estimation; State estimation; Bayesian methods; Filtering; Gaussian noise; Kalman filters; Linear systems; Nonlinear filters; Predictive models; Smoothing methods; State estimation; Upper bound;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1975.1100882