Abstract: The traditional dynamic programming track-before-detect(DP-TBD)algorithm can effectively detect and track targets of uniform linear motion. However, the ignorance of the target’s state transition probability among successive flames results in an incorrect state association and it is easy to be disturbed by the noise when detecting and tracking maneuvering targets. This paper proposes a DP-TBD algorithm based on the hidden Markov model (HMM). The algorithm uses the HMM to describe the motion of the target. A series of hidden states are used to represent the turning rate of the target, which are also estimated and predicted by the hidden state estimation theory of the HMM to the turning rate. The predicted value of the current target state will be obtained at the same time. According to the distance between the predicted state and the next echo data, we calculate the state transition probability. Then this probability is applied to the energy accumulation process of the DP-TBD algorithm to improve the detection and tracking performance. Based on the maneuvering target,the simulation gives the detection and tracking performance of the proposed algorithm, compares the proposed algorithm with the traditional DP-TBD algorithm, the direction-weighted DP-TBD algorithm and the linear least squares DP-TBD algorithm,and the effectiveness of the proposed algorithm is verified.

Key words: track-before-detect (TBD), dynamic programming （DP）, hidden Markov model (HMM), state prediction, state transition probability