Augmented dimension algorithm based on sequential detection for maneuvering target tracking

In order to solve the problem that target tracking algorithm based on single model has poor tracking performance when the target occurs high maneuver and that IMM algorithm has low accuracy in tracking a constant velocity target, an augmented dimension algorithm based on sequential detection for maneuvering target tracking is proposed. First, the KF-UKF joint filtering is proposed. The Kalman filter based on the CV model is used to estimate the state of a constant velocity target. When the target maneuver is detected, the dimension of the CV model is augmented, and the unscented Kalman filter is used to estimate the state. Second, a fading memory sequential detection algorithm is proposed to detect the maneuver. Once the maneuver is detected, the augmented state vector and covariance matrix is compensated so that the modified model can match the actual motion mode. Simulation results show that this algorithm improves the accuracy of tracking by selecting the matching filter depending on the different mode of the target as well as modify the tracking state in real time.