Robust Kalman Filter Using Robust Cost Function

Abstract

Kalman filter is one of the best filter used in the state estimation based on optimality criteria using system model and observation model. A common assumption used in estimation by Kalman filter is that noise is Gaussian but in practically we get thick-tailed Non-Gaussian noise distribution.This type of noise known as Outlier in the data and cause the significant degradation of performance of Kalman Filter. There are many Nonlinear methods exist which can give desired estimation in the presence of Non-Gaussian Noise. We also want the filter which is Robust in the presence of outlier and statistically efficient.But classical Kalman Filter is not suitable in the presence of non-gaussian noise. To get the high statistical efficiency in the presence of outliers, A new robust Kalman Filter is used which can suppress observation, innovation and structural outlier by applying a new type of estimator , known as generalized maximum likelihood estimator.