Performance superiority of CA_TM model over N-P algorithm in detecting χ2 fluctuating targets with four-degrees of freedom

Constant false alarm rate (CFAR) processors play a vital role in organising the heterogeneous detection of fluctuating targets. Specifically, the popular cell-averaging (CA) processor is incapable of maintaining its design false alarm rate when facing clutter with statistical variations. Order-statistics (OS) and trimmed-mean (TM) algorithms have been suggested to robustly estimate the heterogeneous threshold. They have, however, degraded homogeneous performance. For simultaneously exploiting the merits of CA, and OS or TM processors, a hybrid combination of them have been recently proposed. This paper deals with the analysis of these models. Closed-form expression is derived for their detection performance. The primary and outlying targets follow χ2-distribution with four-degrees of freedom in their fluctuation. Our simulation results reveal that the new version CA_TM exhibits a homogeneous performance that outweighs that of Neyman-Pearson (N-P) detector which is employed as a baseline comparison for other techniques in the CFAR world.