Deep Learning-Based Radio Frequency Interference Classification and Mitigation in Radar Images: A CNN and DnCNN Approach

Radio Frequency Interference (RFI) is a most challenging problem faced nowadays for engineers which are dealing with RF and Microwaves, mainly in satellites communication, weather radars and synthetic aperture radar images. This work utilizes the power of Convolutional Neural Network (CNN) for RFI classification from weather radar PPI images and Deep Denoising Convolutional Neural Network (DnCNN) techniques for its mitigation. This study is a first of its kind in India which gives classification and mitigation solution for combating RFI in radar systems using deep learning techniques.