Application of model predictive filtering method in POS for airborne SAR motion compensation system

Position and orientation system (POS) has been used widely to compensate the motional error of airborne synthetic aperture radar (SAR), and its positioning precision has great effect on SAR's imaging directly. In order to obtain higher navigating precision, a method of combining model predictive filter (MPF) and extended kalman filter (EKF) is used in POS. In this method, the model error is assumed unknown and is estimated as part of the solution, and the model error is not limited to Gaussian noise characteristics. This algorithm can be implemented on-line to both filter noisy measurements and estimate the state trajectories. The results of flight tests show that this method has better precision and convergence, especially the position one, than those of KF and EKF. Furthermore, it decreases the computation time by reducing the number of states, and satisfies the requirements of positioning precision and real time which are emphasized in project.