Two CFAR algorithms for interfering targets and nonhomogeneousclutter

The greatest-of-order statistics estimator (GOOSE) constant false alarm rate (CFAR) and the censored greatest-of (CGO) CFAR are described. Both are designed to accommodate interfering targets in the reference window as well as control false alarms in the presence of clutter boundaries. Both succeed in accomplishing these tasks although the CGO-CFAR is preferred as it has a designed graceful degradation as the number of interfering targets exceeds the number of samples censored. The advantage of the GOOSE-CFAR is a theoretical one in that one can derive analytical results for clutter boundary analysis and thus not have to resort to simulations