Order-Statistic Constant False-Alarm Rate (OS-CFAR) processing provides an adaptive threshold to distinguish targets from clutter returns in radar detection. In traditional OS-CFAR, ordered statistics from a fixed-size reference window surrounding the cell under test (CUT) provide an estimate of the mean clutter power. We investigate adapting the reference window size as a function of the observed data in order to obtain robust detection performance in nonhomogeneous clutter environments. Goodness-of-fit tests are used to select the adaptive reference window size. Unlike traditional OS-CFAR, computationally e±cient multiscale OS-CFAR based on this approach must be modified to include the CUT in the reference window. The effects of CUT inclusion are investigated. Preliminary results suggest that CUT-inclusive OS-CFAR with adaptive window size performs well in nonhomogeneous clutter environments of varying size. These results point to the feasibility of computationally efficient multi-scale OS-CFAR.