A DP-TBD algorithm with adaptive state transition set for maneuvering targets
Xinzhe Li, Shouyong Wang, Daikun Zheng · 2016
Traditional dynamic programming Track-Before-Detect (DP-TBD) Algorithms show poor performance in the presence of maneuvering targets due to the constant size of state transition set and the ignorance of state transition probability among successive frames. To cope with this situation, an adaptive state transition set DP-TBD (ASTS-DP-TBD) algorithm is proposed in this paper. The Kalman filter is fused in the multi-frame integration of DP-TBD algorithm and a more accurate state predict is obtained by adding acceleration to the state vector. Based on this predict, the size of transition set is adjusted timely according to the predicted velocity of the target. Besides, the state transition probability is calculated by the state prediction of Kalman filtering. Simulation results prove the performance of the proposed algorithm.