Two-Step Detection Algorithm for Fluctuating Weak Target Based on Dynamic Programming

Abstract

Multi-frame data are processed simultaneously in Track-before-Detect (TBD) algorithm, which is an effective means to improve signal-to-noise ratio. However, some key factors, such as fluctuation loss and multi-frame joint threshold, are neglected when detecting weak target, which leading to detection performance loss inevitably. In order to address the above problems, a novel dynamic programming-TBD (DP-TBD) algorithm based on two-step thresholds is proposed in this paper. Firstly, the multi-frame accumulation amplitude for the observation scene is recalculated based on fluctuation loss analysis and measurement updating, as a result, the accumulation amplitude is closer to the real situation. Then, the first level threshold based on multi-frame data is achieved through the proposed threshold setting method, which avoiding the disadvantage of CFAR algorithm in which the false alarm trajectory cannot be separated effectively from the real target trajectory. Finally, the quantity for false alarm trajectory is decreased further by setting the second level thresholds, which depending on the distribution characteristics of the residual false alarm trajectories. The proposed algorithm takes full advantage of multi-frame joint detection for fluctuating weak targets, which giving consideration to both detection performance and false alarm performance. Simulation results show the effectiveness of the proposed algorithm.