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A novel strategy for inverse synthetic aperture radar imaging based on improved compressive sensing

In actual applications of inverse synthetic aperture radar (ISAR), continuous measurements may be impossible or the collection of data during some periods are not valid in a long coherent processing interval (CPI). Hence, it is significant to study the ISAR imaging strategy with a short CPI. Compressive sensing is a recently proposed technique that allows recovering an unknown sparse signal with overwhelming probability from very limited samples. However, the standard compressive sensing framework has been developed for real‐valued signals. One disadvantage of this method is that any prior phase information is not exploited, which may improve the reconstruction quality by applying some extra constraints. In this paper, a new strategy for ISAR imaging based on improved compressive sensing is proposed, which transforms the ISAR imaging problem into a joint optimization problem over the representation of the magnitude and phase of the complex‐valued scatter coefficient. Because of using phase information in the algorithm, the image quality is improved. Experimental results confirm the effectiveness of the proposed method. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.