Automatic weibull clutter edge localization and target detection based on nonparametric threshold and binary non-coherent integration technique

In this paper, we address the problem of automatic censoring of undesirable clutter edged Weibull reference samples and Swerling II type target detection with constant false censoring and alarm rates (CFCAR). We assume no prior knowledge about the presence or not of a clutter edge. This being, we accomplish both censoring and detection performances through the non-coherent binary integration technique (BIT) over and done with the Weber-Haykin adaptive thresholding combined to a simple censoring procedure. We assess the improved performances of the proposed detector by means of Monte Carlo simulations.