A Kalman filtering-based dynamic programming track-before-detect algorithm for turn target

Dynamic programming-based track-before-detect algorithm is an effective approach to track target with uniform rectilinear or slowly maneuvering motion. But when target performs a turn motion it has a poor performance for the transition step is fixed. In this paper a new algorithm which combines dynamic programming with Kalman filtering has been proposed. This algorithm makes use of state prediction operation in Kalman filtering to change the transition step in DP algorithm adaptively. Numerical results show that this new method has a much better tracking ability than the traditional procedure when target turns.