This paper provides principles and applications of the sparse microwave imaging theory and technology.Synthetic aperture radar(SAR) is an important method of modern remote sensing.During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology,and at the same time encountered increasing complexity in system implementation.The sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory,new system and new methodology of microwave imaging.Based on classical SAR imaging model and fundamental theories of sparse signal processing,we can derive the model of sparse microwave imaging,which is a sparse measurement and recovery problem and can be solved with various algorithms.There exist several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging,including sparse representation,measurement matrix construction,unambiguity reconstruction and performance evaluation.Based on these considerations,the sparse signal processing could be successfully applied to radar imaging,and achieve benefits in several aspects,including improvement of image quality,reduction of data amount for sparse scene and enhancement of system performance.The sparse signal processing has also been applied in several specific radar imaging applications. This paper provides principles and applications of the sparse microwave imaging theory and technology. Synthetic aperture radar (SAR) is an important method of modern remote sensing. During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology, and at the same time encountered increasing complexity in system implementation. sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory, new system and new methodology of microwave imaging. based on classical SAR imaging model and fundamental theories of sparse signal processing , we can derive the model of sparse microwave imaging, which is a sparse measurement and recovery problem and can be solved with various algorithms.There are several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging, including sparse representation, measurement matrix construction, unambigu ity reconstruction and performance evaluation.Based on these considerations, the sparse signal processing could be successfully applied to radar imaging, and achieve benefits in several aspects, including improvement of image quality, reduction of data amount for sparse scene and enhancement of system performance. sparse signal processing has also been applied in several specific radar imaging applications.