Unknown but bounded based input estimation method for Maneuvering Target Tracking problem

Abstract

Input detection and estimation (IDE) methods in Maneuvering Target Tracking (MTT) problem need algorithms for maneuvering detection and covariance resetting. The maneuvering detection causes delay in target state estimation. In this paper, for solving this problem, a state space model is developed with unknown but bounded (UBB) assumption of model uncertainty. In this model, target acceleration is treated as an augmented state in the corresponding state equation. Thereby, an augmented form of state equation is developed. In augmented state equations, the MTT problem is converted to non-maneuvering target tracking problem and standard UBB filter is used for target state and maneuver estimation. To improve the performance of the proposed model, target maneuver is as a Singer like UBB process. In the proposed method, the original state and acceleration vectors are estimated simultaneously. The proposed target-tracking algorithm operates in both non-maneuvering and maneuvering modes. The theoretical development is verified by simulation results, which also contain some examples of tracking typical target maneuvers. The results are compared with traditional IE and MIE methods. The comparison shows the better performance of the proposed method.