Automatic WH-based edge detector in Weibull clutter

Abstract

Assuming a non-stationary Weibull background with no prior knowledge about the presence or not of a clutter edge, we propose and analyze the censoring and detection performances of the automatic censoring Weber-Haykin constant false censoring and alarm rates (ACWH-CFCAR) detector in homogeneous clutter and in the presence of a clutter edge within the reference window. The cfcarness property is assured by use of the Weber-Haykin (WH) adaptive thresholding which bypasses the estimation of the distribution parameters. The censoring algorithm starts up by considering the two most left ranked cells and proceeds forward. The selected homogeneous set is used to estimate the unknown background level. Extensive Monte Carlo simulations show that the performances of the proposed detector are similar to those exhibited by the corresponding fixed-point censoring WH-CFAR detector.