2026-10-14T15:40:13Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32826311 2026-10-01T16:09:01Z portal_1172 item_type_8 month_year_10_2026

Deep learning in compressive radar imaging and systems Jiaming Zhang (346789) PUREID: 686966484 Computational Microwave Imaging deep learning generative adversarial network sensing matrix Conventional microwave imaging methods, including synthetic aperture radar (SAR), suffer from slow data acquisition and high hardware complexity, limiting their suitability for real-time imaging, particularly for electrically large scenes. 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. Together, these approaches provide a coherent framework that directly addresses the aforementioned challenges and advances the state of the art in CMI reconstruction.The proposed approaches significantly reduce the computational effort associated with transfer-function estimation, remove the need for labor-intensive near-field measurements, enable accurate joint reconstruction and recognition, and effectively recover target information in obstructed scenarios.Overall, the contributions of this thesis establish a deep learning–driven foundation for efficient, scalable, and resilient CMI, supporting its advancement toward real-time deployment in practical environments. 2026-10-01T16:09:01Z Text Thesis 10.17034/32826311.v1 https://figshare.com/articles/thesis/Deep_learning_in_compressive_radar_imaging_and_systems/32826311 All Rights Reserved