Performance Analysis of Order-Statistic CFAR Detectors in Weibull Background: A comparison

Abstract

This paper deals with the problem of automatic target detection in Weibull clutter and multiple target situations, without any prior knowledge of neither the non-stationary clutter statistics in which the radar operates nor the number of outliers that may be present in the reference window. We study and compare the performances of the Forward Order Statistic Automatic Censoring and Detection Constant False Censoring and Alarm Rates Detectors based upon the Maximum Likelihood Estimators (MLE-based F-OSACD-CFCAR) and the Weber-Haykin threshold (WH-based F-OSACD-CFCAR). The censoring and detection algorithms are a two in one built detector. They select repeatedly a suitable set of ranked cells among reference cells surrounding the cell under test to estimate the unknown background level and set the adaptive threshold accordingly. The performances are evaluated by means of Monte Carlo simulations.