LAUSR

The Weibull distribution is commonly used for clutter modeling as it can provide a good fit to the experimental data over a wide range of conditions, and as such there is considerable attention to the development of constant false alarm rate (CFAR) detectors under such a clutter model assumption. The detection backgrounds of some radar systems are nonhomogeneous, which is a challenging task for CFAR detection. The variability index CFAR (VI-CFAR) detector is proposed for the nonhomogeneous Gaussian clutter in the literature. However, it might increase the false alarm rate significantly in the Weibull clutter due to the target-like clutter samples, and its performance degrades in the nonhomogeneous clutter with the presence of interfering targets at both sides of the cell under test. To address these problems, we propose a robust variability index CFAR detector for the Weibull background (RWVI-CFAR) with known shape parameters in this article. In the RWVI-CFAR, we extend the VI-CFAR to the Weibull clutter and design an automatic outlier censoring maximum likelihood CFAR (AOCML-CFAR) strategy to improve the detection performance in multiple-target situations. The proposed AOCML-CFAR strategy exploits the sparsity of interfering targets to censor the outliers adaptively. By selecting the AOCML-CFAR strategy, the anti-interference capability of the RWVI-CFAR is significantly improved. Simulated and experimental results show the favorable and robust performance of the RWVI-CFAR in the Weibull background.