State estimation in the presence of non-Gaussian noise

The problem of nonlinear filtering with a non-Gaussian model of measurement errors is considered. Based on Bayes classification of the observations an approximate solution is introduced. The Bayesian estimator can be applied to any discrete time, linear, or nonlinear system which is observed in additive non-Gaussian measurement noise. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the approximate filter outperforms currently used approaches and at the same time offers simplicity in the design