ISAR reconstruction from incomplete data using total variation optimization

Sparsity of the ISAR images is exploited with the aim to use the possibility of applying an under-sampling strategy as assumed by the compressive sensing approach. The signal sparsity is a desirable property that needs to be satisfied in order to reconstruct the signals and images from the compressive sensed data. It is assumed that certain amount of radar data is not available and the idea is to reconstruct the radar image from the rest of the data. The signal samples are observed in the spatial domain, and the reconstruction is based on the total variation minimization. The procedure is tested on both, synthetic and real ISAR image, showing satisfactory reconstruction quality with a small set of acquired samples.