This paper addresses the problem of computational efficiency with respect to Dynamic Programming (DP) based Track-Before-Detect (TBD). Generally, DP-TBD is a grid-based method that estimates target trajectory by means of searching all the physically admissible paths in a finite discrete state space. However, its computational complexity increases nonlinearly as the state space expands with size or dimension, which heavily restricts its applications for many realistic scenarios, e.g., radar target detection. To alleviate this problem, a fast two-step implementation of DP-TBD is proposed in this work. In the first step, to reduce the computational costs, target states are roughly estimated by searching a discrete grid with larger cell size. In the second step, an accurate search is performed only on the parts of the state space indicated by the results in the first step with reasonable computational cost. Additionally, the proposed algorithm is also suitable for the surveillance radar scenarios. Simulation results show that the proposed algorithm can effectively improve the computational efficiency of DP-TBD with limited performance degradation.
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