The best linear unbiased with greatest of selection (BLUGO) CFAR algorithms

This work presents a new CFAR detector with greatest of selection based on best linear unbiased method (BLUGO-CFAR). This CFAR detector has the ability of CFAR algorithms with greatest of selection to control the rise of false alarm rate at clutter edge, it also has the advantage of CFAR algorithms based on order statistics in multiple targets situation. The analytic results show that the performance of BLUGO is evidently superior to that of OSGO both in homogeneous background and in multiple targets situations, resulting by an increase in the number of order statistics to estimate the noise power level. Since the reference window is split into two sub-windows, the sample sorting time is reduced as half as that of OS. If Ml=M2=0, Nl=N2=0, BLUGO reduces to GO, if M1=N1=O, BLUGO reduces to MX-CMLD.