Iterated minimum upper bound filter for tracking orbit maneuvering targets

In this paper, the movement of a maneuvering low earth orbit satellite is modeled by a nonlinear stochastic system with unknown disturbance input, and an Iterated Minimum Upper Bound Filter is proposed to decrease the upper bound of the covariance of estimate errors via iterative optimization. The Monte Carlo simulation shows that the proposed filter significantly reduces the peak estimation errors due to orbit maneuvers compared with the well-known interacting multiple model method. Besides, it can accurately detect the target maneuvering time instant through thresholding the estimated fading factor.