Hankel Matrix Completion Approach for High-resolution ISAR Imaging with Sparse Aperture

Inverse synthetic aperture radar (ISAR) imaging is very challengeable when facing with sparse aperture (SA). Traditional compressive sensing (CS) methods handles the SA imaging problem using sparse representation, but will cause model mismatch inherently. In this paper, a Hankel matrix completion (HMC) approach is proposed for SA ISAR imaging, which can effectively enhance the imaging performance with grid-less technique. The Hankel matrices are constructed from the SA echo in each range bin. After the low-rank property is proved, an MC method based on an augmented Lagrange multiplier (ALM) solution is used to reconstruct the cross-range profile. The proposed method based on the low-rank constraint can avoid the presupposition of over complete basis, thus effectively overcoming the off-grid effect of CS methods. The robustness and effectiveness are further vali-dated using experimental analysis using measured data.