An EVI-ASD-CFAR Processor in a Pareto background and multiple target situations

Abstract

This paper investigates the problem of automatic target detection in a Pareto background under multiple target situations. The number of interfering targets is assumed to be unknown. We derive the Enhanced Variability Index Automatic Selection and Detection Constant False Alarm Rate (EVI-ASD-CFAR) Processor. This latter selects and matches dynamically the suitable detector among the Geometric Mean (GM)-CFAR, Greatest Of(GO)-CFAR and Trimmed Mean (TM)-CFAR. The unknown background level is then estimated and set to the corresponding threshold. The detection performances of the proposed processor are assessed via Monte Carlo simulations.