High-Resolution ISAR Imaging of Highly Maneuvering Targets in 3-D Rotational Motion via Frame Selection and Sparse Aperture Imaging

Inverse synthetic aperture radar (ISAR) imaging is useful for classifying targets of interest. Therefore, it is critical to enhance Doppler resolution, which depends on the uncontrollable target’s motion. Traditional range-Doppler processing can generally compensate for translational motion (TM) and two-dimensional rotational motion (2D-RM) to focus ISAR images. However, it is nearly impossible to compensate for the three-dimensional rotational motion (3D-RM) of highly maneuvering targets. To solve this problem, frame selection approaches have been devised to retain frames with 2D-RM only, while discarding frames with 3D-RM. However, conventional frame selection methods usually have limited Doppler resolution against highly maneuvering targets, because each selected time frame with 2D-RM is often very short. This paper proposes an imaging framework to overcome the aforementioned 3D-RM problems, by exploiting frame selection and sparse-aperture (SA)-ISAR imaging simultaneously. The proposed approach first identifies time frames with dominant 2D-RM. Notably, time frames with 2D-RM consist of non-uniform samples along the slow time. This problem poses challenges for adopting conventional RM compensation methods. The proposed framework compensates for the nonlinear 2D-RM on non-uniform grid by employing dictionary estimation using the parametric signal reconstruction (DEPSR). Experimental results for both the simulated and real measurement datasets demonstrate the effectiveness of the proposed framework.