On Maximum Likelihood Quantile Matching CFAR Detection in Weibull Clutter and Multiple Rayleigh Target Situations: A Comparison

A recent survey has revealed that radar researchers still encounter difficulties to develop detectors that cope with non-Gaussian backgrounds. On another note, in real-world applications, many modeling research papers show a high agreement between radar sea clutter data and the Weibull distribution. In this paper, we propose and analyze the Quantile Matching Constant False Alarm Rate (QM-CFAR) detector for a Weibull background. Specifically, assuming a non-stationary Weibull clutter with the presence or not of interfering targets, the Quantile Matching (QM) and the Maximum Likelihood Estimator (MLE) are concomitantly used to allow the proposed detector to perform robustly in multiple target situations with a priori unknown Weibull parameters. By that means, we first rank order the reference samples to select quantile information that shares the same clutter parameters as the Cell Under Test and eliminate any outliers within the data. Then, we resort to the QM and the MLE to get the parameters. Finally, we carry out target decision-making. The subsequent CFAR detection threshold allows then fixed censoring of the upper end of the reference window. Monte-Carlo simulations show that, compared to recent existing CFAR algorithms, the QM-CFAR detector provides robust and accurate estimates of the Weibull distribution parameters and achieves less degradation of the PD (Detection Probability) in multiple target situations.