An effective ISAR autofocus algorithm based on single eigenvector

Phase autofocus is a key step of translational motion compensation (TMC) in inverse synthetic aperture radar (ISAR). In this paper, an effective phase autofocus algorithm for ISAR is proposed based on single eigenvector. Samplings in multiple range bins are used to construct the covariance matrix and obtain the eigenvector corresponding to the largest eigenvalue. It is found that this eigenvector can act as the phase compensation vector for ISAR autofocus. Compared to the conventional methods, the proposed algorithm can obtain a better focused image especially in a low SNR case, as long as there is a relatively prominent scatterer in the target. Finally, experimental results based on real measured data are provided to demonstrate the effectiveness of the proposed method.