Robust Sliding Window CFAR Detection Based on Quantile Truncated Statistics

In this article, the concept of quantile is introduced and elaborately related to the truncation depth, based on which quantile truncated statistics (QTS) is put forward. The QTS gives a reasonable explanation of truncation depth and makes the selection of truncation depth well-founded and controllable. In addition, maximum likelihood (ML) estimation based on QTS (QTS-MLE) for the probability density function (pdf) parameters is derived. We start the analysis from Weibull background assuming that the shape parameter is known, and then extend it to the case where the shape parameter is unknown. By analyzing the variance and mean square error (MSE) of the estimated parameters in Weibull background, it is found that QTS-MLE has better estimation performance than the MLE based on TS (TS-MLE). On this basis, the constant false alarm rate (CFAR) detector based on QTS-MLE, i.e., QTS-CFAR, is proposed. The analytic expressions of the false alarm rate and detection probability of QTS-CFAR are derived under the Weibull background with known shape parameter. The full CFAR characteristics of TS- and QTS-CFAR detectors in Weibull background with unknown shape parameter are proved by an invariant theory. Monte Carlo simulations show that the QTS-CFAR detector has better antiinterference performance and false alarm control ability in multiple-target environment. Furthermore, the superiority of QTS-CFAR detector is verified by the real data collected by skywave over-the-horizon radar (OTHR). Finally, we present the expression of QTS-MLE for the scale parameter in the Gamma background.

Automated summary

This article introduces the concept of quantile and its relation to truncation depth, and proposes quantile truncated statistics (QTS) based on this concept. QTS provides a reasonable explanation for truncation depth and allows for well-founded and controllable selection of the depth. Additionally, the article derives maximum likelihood estimation (MLE) based on QTS (QTS-MLE) for the parameters of the probability density function (pdf). The article also explores the performance of QTS-MLE in estimating parameters in Weibull background, and proposes the QTS-CFAR detector based on QTS-MLE.