Detection performance of the trimmed-mean CFAR processor with noncoherent integration

The ordered-statistic constant false alarm rate (OS-CFAR) detector has some advantages over the cell-averaging (CA) CFAR scheme, especially in cases where more than one target is present within the reference window or where this reference window is crossing clutter edges. The trimmed-mean (TM) CFAR processor is a more generalised OS-CFAR scheme which combines ordering and arithmetic averaging. The author analyses the detection performance of the TM-CFAR scheme when noncoherent integration is used under the chi-square target fluctuation model. For specific values of the trimming parameters, the performance of the TM-CFAR processor reduces to that of the CA-CFAR and OS-CFAR schemes. Results are depicted for the Swerling case II target model and for a homogeneous environment. As the number of noncoherently integrated pulses increases, lower threshold values and better performances are obtained for the three schemes considered.