Clutter suppression and target detection based on biparametric clutter map CFAR

Clutter map constant false alarm rate (CFAR) detectors have been utilized in radar systems, which usually uses local threshold estimates from background observations to maintain CFAR level. However, when the local observations contain irrelevant information and/or heavy clutter, its detection performance would be severely degraded. To solve the problem in that clutter-dominated environment, a biparametric clutter map CFAR detection method is proposed, and the live observations from clutter environment is used for evaluating detection performance. Firstly, the mean and standard deviation of clutter map cells are updated through two single pole loop integrators respectively. Secondly, the local threshold estimation is obtained based on the mean and standard deviation to maintain the overall clutter statistics. Due to the standard deviation of clutter map is considered, the proposed method have better applicability to the real clutter environment than monoparametric clutter map method. Simulation experimental results show that the proposed method performs better than monoparametric clutter map method with better target detection ability.