On the Performance of Negentropy Approximations as Test Statistics for Detecting Sinusoidal RFI in Microwave Radiometers

Radio-frequency interference (RFI) is a persistent threat to Earth-observing microwave radiometers. A number of test statistics are used for radiometric RFI detection. This paper presents a new RFI detection method that uses the information theoretic quantity known as negentropy. In particular, we study six negentropy-based test statistics and compare their performance against kurtosis, Jarque-Bera, Anderson-Darling, and Shapiro-Wilk normality tests for specific RFI signal models. The Neyman-Pearson decision rule is used to develop receiver operating characteristic curves for each test statistic. We show that although negentropy can be used to detect RFI, it does not outperform kurtosis, except for the kurtosis blind-spot case.