Robust adaptive Kalman filtering for target tracking with unknown observation noise

Abstract

The Kalman filter (KF) is widely used in the field of target tracking. In practical target tracking systems through, the observation noise is often unknown and characterized by heavier tails named outliers. That will affect the performance of target tracking seriously and even lead to filtering divergence. To overcome this problem, a novel robust Kalman filter (RKF) is proposed based on the maximum a posteriori (MAP) estimation to observation outliers. In addition, the adaptive estimate of observation noise variance R is also given based on the weighted correlation innovation (WCI) sequences of output of a steady state Kalman filter (SSKF). Finally, a robust adaptive Kalman filter (RAKF) algorithm is raised by implementing RKF and adaptive estimate of R simultaneously. The feasibility of the algorithm is demonstrated by an example of target tracking with simulation.