A Weber-Haykin Based Automatic Censoring and Detection in Weibull Background

Abstract

In this paper, we study 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. In doing this, we develop the Forward/Backward Order Statistic Automatic Censoring and Detection Constant False Censoring and Alarm Rates Detectors based upon the Weber-Haykin adaptive threshold (WH-based F/B-OSACD-CFCAR). These detectors 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 censoring and detection performances are evaluated by means of Monte Carlo simulations.