QQ-PLOT APPROACH TO ROBUST KALMAN FILTERING

Noise distribution arising in certain applications frequently deviates from the assumed gaussian model, often being characterized by heavier tails generating the outliers. Since, in the presence of outliers, th e performance of a Kalman filter can be very poor, a statistical appro ach-named QQ-plot-is suggested to make the Kalman filter more robust. In addition, the first and the second-order moments of noise processes are estimated simultaneously with the system states, using the QQ-plo t of the noise samples generated in the 'robustified' Kalman filter al gorithm. Results of simulation demonstrating the robustness of the pro posed state estimators are also included.