Robust estimation with unknown noise statistics

Abstract

The equivalence between the Kalman filter and a particular least squares regression problem is established and the regression problem is solved robustly using a statistical approach, named M-estimation. M-robust estimators are derived for adaptive estimation of the unknown a priori state and observation noise statistics simultaneously with the system states. The feasibility of the approach is demonstrated with simulation.