Adaptive censoring maximum likelihood CFAR detector in Weibull clutter

An adaptive censoring maximum likelihood constant false alarm rate detector (ACML-CFAR) based on ordered data variability (ODV) is researched in spiky Weibull clutter environment. The ACML-CFAR employs ODV-based censoring algorithm to censor several reference samples, and then employs the rest to estimate the parameters of Weibull clutter through maximum likelihood estimator. Censoring efficiency of the ODV-based censoring algorithm in Weibull clutter and interfering targets environments is evaluated. And then the detection performance of ACML-CFAR is evaluated and compared with that of ML-CFAR in Weibull clutter and interfering targets environments through computer simulations. It shows that the detection performance of ACML-CFAR is slightly better than that of ML-CFAR in Weibull clutter. In interfering targets environments, when the number of censoring samples is less than the number of interfering targets, the detection performance of ML-CFAR degrade severely, whereas ACML-CFAR can censor interfering targets adaptively and effectively, and doesn´t need any prior information about the number of interfering targets, and therefore performs robustly. It can be used in actual environments that include unknown interfering targets.