Deep learning in compressive radar imaging and systems

To address these challenges, computational microwave imaging (CMI) is considered an alternative technique. CMI-based systems successfully achieve a physical layer compression, but are still limited by the computational burden in the signal processing layer.

To improve the efficiency of CMI-based systems, deep learning techniques have been explored to reduce the computational burden associated with conventional methods.In this thesis, a series of deep learning–based methodologies are developed to enhance the overall efficiency of CMI. These include two ways of transfer function prediction, a deep learning approach for multi-tasking in CMI application, and a deep learning-based reconstruction method capable of handling obstructed measurement conditions.