ISAR imaging based on 2D tensor sparse signal recovery algorithm

In Inverse Synthetic Aperture Radar (ISAR) imaging, with a short coherent processing interval (CPI), the relative movement of the target can be approximated precisely as uniform rotation, then imaging of maneuvering target can be implemented. At the same time, it is better for real time imaging. By using the sparse property of the strong scatterers and the sparse signal recovery algorithm, the image of a target can be improved. The received signal of ISAR is two dimensional. Transforming the two dimensional equation to one dimensional equation then solving linear equation based sparse signal recovery problem requires huge memory and computational budget. By using two dimensional compressive sensing signal recovery algorithm, which saves memory and computing load, the ISAR image is recovered with better quality than the conventional FFT based method.