Radio Frequency Interference Detection in Microwave Radiometry: A Novel Feature-Based Statistical Approach

The amount of radio frequency interference (RFI) present in microwave radiometer observations is significantly increasing over time. The presence of RFI in radiometer measurements impacts the computation of crucial geophysical parameters of the Earth's surface and atmosphere. In this paper, we introduce the idea of using heterogeneous feature-based representation for baseband radiometer measurements. The feature values are estimated empirically using statistical methods such as maximum likelihood estimator (MLE) and Monte Carlo experiments. Further, we also implement a feature selection algorithm that selects the most discriminant features. The proposed approach reviews the features sequentially to determine the final decision based on maximum a posteriori (MAP) estimation. The performance evaluation of the proposed approach with the traditional RFI detection methods shows that the proposed approach has a higher ability to detect RFI even when the interference to noise ratio (INR) of the RFI is as low as-20 dB.