OS-CFAR Based on Thresholding Approaches for Target Detection

Test cell statistics is widely used for constant falls alarm rate (CFAR) based thresholding approaches. The main function of CFAR algorithm is to detect target when the return signal consist of noise, interference and clutter. The property of constant false alarm rate to maintain approximately constant rate of false alarm keeping probability of detection is used. Adaptive threshold plays vital role in detection probability targets. The CA-CFAR, GO-CFAR, SO-CFAR, OS-CFAR etc. are the types of CFAR. “Cell average” (CA-CFAR) performance degrades in the presence of interfering target or change in background clutter noise. “Greatest Of” (GO-CFAR) is designed to maintain constant value of false target detection during reverberation edges and is unable to detect target which is closely spaced. “Smallest Of” (SO-CFAR) performance is better in case of multiple target. But none of them perform well in all three background environment such as homogeneous, reverberation edges and multiple target. “Ordered Statistic” (OS-CFAR) composed of Cell average CFAR, Smallest of CFAR and Greatest of CFAR performs well in presence of homogeneous, reverberation edges and multiple target environment respectively.