Microwave Radiometer RFI Detection Using Deep Learning

Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity. The Soil Moisture Active Passive (SMAP) mission has an aggressive approach RFI detection and filtering using dedicated spaceflight hardware ground processing software. As more sensors push observe at larger bandwidths in unprotected or shared spectrum, continues be essential. This article presents deep learning SMAP spectrogram data as input images. study utilizes the benefits transfer evaluate viability this method radiometers. well-known pretrained convolutional neural networks, AlexNet, GoogleNet, ResNet-101 were investigated. provided highest accuracy with respect validation (99%), while AlexNet exhibited agreement (92%).