Trimmed Mean-based Automatic Censoring and Detection in Pareto background

In this paper, we study the problem of automatic target detection in Pareto clutter and multiple target situations with the assumption of no prior knowledge of the number of outliers that may be present in the reference window. In doing this, we develop the Trimmed Mean-based Automatic Censoring and Detection Constant False Censoring and Alarm Rates Detector (TM-based-ACD-CFCAR). This detector select repeatedly a suitable set of ranked cells, among the reference cells surrounding the Cell Under Test (CUT), 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.