An eigenvector approach for inverse synthetic aperture radar (ISAR) motion compensation and imaging

Inverse synthetic aperture radar (ISAR) is a high resolution imaging radar. The image reconstruction of ISAR involves two steps; motion compensation and image formation. In this paper, an eigenvector approach is proposed to carry out ISAR motion compensation. The eigenvector approach is identified as the maximal likelihood (ML) phase estimation of translational motion. The relationship of the eigenvector approach to other methods is also investigated. The results of processing simulated data and real data demonstrate that the eigenvector approach is correct and effective.