A New Class of Adaptive CFAR Methods for Nonhomogeneous Environments

The paper introduces a new class of adaptive CFAR methods to cope with the problem of outliers due to the presence of clutter edges and interfering targets. A fundamental distinction between the proposed approach and existing adaptive CFAR approaches is that in order to maintain robust performance the former uses information on positions at which estimated outlier-free cells appear in the full reference window and the statistics of the sample in the cell under test. The performance of one of the possible implementations of new adaptive CFAR methods is studied and compared with that of an existing adaptive CFAR approach. The results show significant advantages of the proposed class of adaptive CFAR methods in both the false alarm regulation property and detection performance.