Abstract:

An intelligent constant false alarm rate (CFAR) detector with better robustness performance is proposed. This CFAR detector is based on maximum likelihood difference (MLD) and is named a MLD-CFAR detector. In this detector, the heterogeneous clutter estimation CFAR (HCE-CFAR) with a high detection probability and the variability index CFAR (VI-CFAR) with a low false alarm rate are combined. The MLD and mean ratio are computed to distinguish the clutter environments of the leading and lagging windows, and then the corresponding reference cells and the mean level CFAR algorithm are selected adaptively to calculate the detection threshold. The performance of the proposed MLD-CFAR detector is evaluated and compared with other CFAR detectors by numerical simulation. It shows that the MLD-CFAR keeps a constant false alarm rate with higher probability of detection and lower probability of target masking in clutter edge environments, and is with a lower CFAR loss in homogeneous clutter environments. Experimental results from a linear frequency modulated continuous wave radar system verify the correction and efficiency of the proposed MLD-CFAR detector.