Maneuvering Target Tracking Using Delayed Update Filter

Abstract

A Kalman filter with delayed update filter (DUF) is proposed for mining the steady-state performance when tracking a constant input/bias target, while its transient performance is maintained when maneuver occurs. The key of this scheme is using a constant input/bias constraint as a pseudo-measurement to update current states estimated via Kalman filter. This constraint can remove the influence caused by mismatch of common dynamic models. Simulation results are given for comparing with the variable dimension filter and interactive multiple model filter. Benefit and validity of the proposed method is verified