A Multi-frame Association Dynamic Programming Track-before-detect Algorithm Based on Second Order Markov Target State Model

Traditional Dynamic Programming Track-Before-Detect (DP-TBD) algorithms use only observation data of current frame to associate with merit function and accumulate energy at each stage of data association. The ignorance of targets state relevance among successive frames and its own kinematic characters results in false state association at low Signal-to-Noise Ratio (SNR), which reduce detecting and tracking performance profoundly. To solve this issue, a DP-TBD algorithm based on second order Markov target state model is proposed. Taking maximum of the targets state conditional PDF ratio as the optimal criteria, this algorithm makes use of second order Markov model to describe the targets state relevance and defines a state transition probability model according to targets kinematic characters, which relates to targets turning angle. On these bases, a multi-frame data association DP-TBD algorithm is implemented. Compared to traditional DP-TBD algorithm through a simulation experiment, the proposed algorithm turns out to have better detection and tracking performance.