AN ADAPTIVE CONSTANT FALSE ALARM DETECTION METHOD BASED ON BACKGROUND DISCRIMINATION

In this paper, we propose an adaptive constant false alarm detection method based on background Discrimination (BBD-CFAR) to address the issues of degraded performance in detecting clutter edges and multi-neighbor targets. The proposed method utilizes non-uniform clutter estimation to maintain a constant false alarm rate and introduces mean ratio and clutter power interval to discriminate the detection background. We use an iterative approach to improve the detection probability of multi-neighbor target backgrounds. The BBD-CFAR method is applied to clutter backgrounds with different powers and multi-neighbor target backgrounds. Its ability to suppress neighboring target interference and improve detection performance at clutter edges is evaluated through Monte Carlo simulations. The experimental results demonstrate that the proposed BBD-CFAR method has superior detection performance in multi-neighbor target backgrounds and clutter edges.