Extended target tracking using an IMM based Rao-Blackwellised unscented Kalman filter

Abstract

An extended target tracking problem for high resolution sensors is considered. An ellipsoidal model is proposed to exploit sensor measurement of target extent, which can provide extra information to enhance tracking accuracy, data association performance, and target identification. Due to the presence of high nonlinearity of the model, a Rao-Blackwellised unscented Kalman filter (RBUKF) is adopted in this paper. In contrast to the most commonly used extended Kalman filter (EKF), the RBUKF provides more accurate and reliable estimation performance, without increasing any computational complexity. An interacting multiple model (IMM) technique is combined with the RBUKF method to adapt the target maneuver and motion mode switching problem which is vital for nonlinear filtering. The developed IMM-RBUKF algorithm on extended target tracking problem is validated and evaluated by computer simulations.