Dynamic programming track-before-detect algorithm for radar target detection based on polynomial time series prediction

摘要

An improved dynamic programming track-before-detect (DP-TBD) algorithm is proposed in this study. A new relaxed DP-TBD test statistic containing a term of state transition probability is derived. The state transition probability is designed according to the one-step prediction of the target state. An asymptotic and recursive solution is developed to obtain the state prediction by the polynomial time series model under the framework of weighted least squares. The impact of the weight parameter on the performance of the proposed algorithm is also investigated. The proposed algorithm can efficiently integrate the energy back-scattered along the admissible target trajectory in that the designed state transition probability enables the relaxed test statistic to distinguish real targets from the false ones more effectively. The prediction needs no priori information of target state space model and can be embedded in the recursion of the DP-TBD. Numerical simulations are provided to assess and compare the performance of the proposed algorithm. It turns out that the proposed algorithm has better detection and tracking performance than the basic one and is resilient to various target motion forms.