A Computationally Efficient CFAR Algorithm Based on a Goodness-of-Fit Test for Piecewise Homogeneous Environments

摘要

A new constant false alarm rate (CFAR) detection algorithm operating in nonhomogeneous clutter is proposed. The proposed CFAR algorithm, cell-under-test (CUT) inclusive (CI) CFAR, utilizes a goodness-of-fit test for determining and combining homogeneous windows, which results in a higher detection performance. The CI-CFAR algorithm layout and analytical properties for the ideal case are investigated. In comparison with the traditional cell-averaging (CA) CFAR and order-statistic (OS) CFAR, it is demonstrated that CI-CFAR outperforms both in most situations.