A Robust Censoring Two-dimensional CFAR Detector Based on Ordered Data Variability

摘要

In this paper, we propose a two dimensional CFAR detector based on ordered data variability for multiple target situations. The automatic censored ordered data variability detectors (AC-ODV) select dynamically, by performing successive hypothesis test. It doesn't require any prior information about the background environment and uses the variability index (VI) statistic, as a shape parameter, to accept or reject the ranked cells under investigation. This dynamic test is applied for robustness enhancement of all censored CFAR schemes, after the novel super accurate two dimensional (2D) confirmation test is applied for target declaration.