Performance analysis of dual automatic censoring and detection in heterogeneous Weibull clutter: A comparison through extensive simulations

In this paper, we address the problem of lower and upper automatic censoring of unwanted samples from a rank ordered data of reference cells, i.e., dual automatic censoring, and target detection with constant false censoring and alarm rates (CFCAR). Assuming a non-stationary background with no prior knowledge about the presence or not of any clutter edge and/or interfering targets, we propose and analyze the censoring and detection performances of the dual automatic censoring best linear unbiased (DACBLU) CFCAR detector in homogeneous and heterogeneous Weibull clutter. The cfcarness of both censoring and detection algorithms are guaranteed by use of linear biparametric adaptive thresholds. That is, we introduce a logarithmic amplifier, and determine the transformed Gumbel distribution parameters through the Best Linear Unbiased Estimators (BLUEs). 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 automatic censoring method used in conjunction with various CFAR detectors are similar to those exhibited by their respective fixed-point(s) censoring detectors. Moreover, its performances are even better than those related to automatic censoring methods based on the assumption of initial homogeneous population.