Maritime 3D-ISAR with Clutter Suppression

Three dimensional (3D)-ISAR has been developed to improve the target representation and provide more accurate estimates of a target’s geometric features. For example, after the dominant scatterers are extracted from the ISAR imagery, the length, width and height can be determined and used for classification. Unfortunately, if there is significant sea clutter in the scene, the scatterer extraction process will be confused by both the sea clutter and target returns. In this paper, we use both simulated and real data to demonstrate an efficient dictionary learning technique that suppresses the sea clutter prior to image formation and improves the robustness of the imaging process.