Characterization and Removal of RFI Artifacts in Radar Data via Model-Constrained Deep Learning Approach

Microwave remote sensing instruments such as synthetic aperture radar (SAR) play an important role in scientific research applications, while they suffer great measurement distortion with the presence of radio frequency interference (RFI). Existing methods either adopt model−based optimization or follow a data−driven black−box learning scheme, and both have specific limitations in terms of efficiency, accuracy, and interpretability. In this paper, we propose a hybrid model−con-strained deep learning approach for RFI extraction and mitigation by fusing the classical model-based and advanced data-driven method. Considering the temporal-spatial correlation of target re-sponse, as well as the random sparsity property for time−varying interference, a joint low−rank and sparse optimization framework is established. Instead of applying the iterative optimization process with uncertain convergency, the proposed scheme approximates the iterative process with a stacked recurrent neural network. By adopting this hybrid model−constrained deep learning strategy, the original unsupervised decomposition problem is converted to a supervised learning problem. Ex-perimental results show the validity of the proposed method under diverse RFI scenarios, which could avoid the manual tuning of model hyperparameters as well as speed up the efficiency.