High-resolution radio frequency interference detection in microwave radiometry using deep learning

The success of microwave radiometry depends on how accurately it can measure the natural emission of the Earth without the effects of unwanted signals. The consequence of unwanted signals in radiometers is known as radio frequency interference (RFI). The high intensity of these corrupted signals, along with wider bandwidth and longer duration, may jeopardize the overall success of a mission. These reasons resulted in a need for a robust RFI detection algorithm that will enable the mitigation of the contaminated portions of the measurements. Attributes related to RFI could be very dynamic, making it very difficult to detect with a particular algorithm. To address this issue, deep learning (DL) could be an attractive solution to detect RFI with the help of time-frequency analysis, i.e., spectrograms of the received measurement. This study aims to detect and localize RFI in a particular time-frequency bin of spectrograms with the help of DL to retrieve the non-contaminated portion of the measurements.