A Dynamic Programming Track-Before-Detect Algorithm with Adaptive State Transition Set

Due to the use of a fixed-size state transition set, the traditional dynamic programming Track-Before-Detect (DP-TBD) algorithm significantly reduces the detection and tracking performance of maneuvering targets. This paper proposes a DP-TBD algorithm with an adaptive state transition set (ASTS-DP-TBD). The algorithm improves the search efficiency of the maneuvering target by introducing Kalman filtering and target state transition probability in the traditional algorithm. In addition, this paper also optimizes the termination decision strategy of the algorithm, which significantly improves the detection performance. Simulation results show that the proposed algorithm in this paper has better detection and tracking results than traditional algorithms for maneuvering targets.