Collaborative Compressive Radar Imaging With Saliency Priors

Although several works have been done on high-resolution inverse synthetic aperture radar (ISAR) imaging via compressive sampling technology, they only explore sparse priors of targets in the scene and the image is recovered cell by cell separately. In order to potentially enhance targets and suppress clutters for fast and higher quality imaging, in this paper, we advance a collaborative compressive ISAR (CC-ISAR) imaging approach, by exploring both sparse priors and saliency priors of targets. First, a geometric saliency map is derived by performing pulse contourlet transform on a preliminary image. Then, a graph Laplacian is constructed to regularize a multiple measurement vector problem for collaborative compressive radar imaging. Third, targets are approximately separated from the background in the saliency map, and salient weights are defined for the target and background, respectively, to derive a saliency weighted $l_{1}$ -norm optimization algorithm. Some experiments are taken on real ISAR data to evaluate the performance of the proposed method, and both visual results and numerical guidelines prove that CC-ISAR imaging method can obtain more accurate targets and outperform its counterparts.