An Adaptive Kalman Tracking Filter and It's Application to Tether Satellite

Abstract

An Kalman filter has been widely used in target tracking. In most circumstances, the exact values of model parameters, however, are unknown and adaptive filtering is necessary. In this paper, a novel target tracking approach based on adaptive Kalman filter is proposed, in which unknown process noise variance is estimated by using the measured data of an actual state. The suggested approach has certain merits over various existing adaptive algorithms and it is simple, efficient and suitable for real-time applications. As an example, the tracking of a tethered subsatellite is analyzed, and the performance of this approach is evaluated by a simulation study on a realistic system.