ISAR maneuvering targets imaging and motion estimation from parametric sparse bayesian learning

Abstract

Recently, compressive sensing theory has been successfully applied in inverse synthetic aperture radar (ISAR) imaging. However, the issue of maneuvering target imaging from compressive sampling data has not been sufficiently addressed because it is difficult to jointly deal with both sparse imaging and motion compensation under compressive sampling. In this paper, we develop a novel algorithm of high-resolution ISAR imaging for maneuvering targets from compressive sampling data. In this algorithm, a non-uniform scaled Fourier dictionary is constructed to represent the maneuverability. A hierarchical statistical model is utilized to encode the sparsity of ISAR image. Then, ISAR imaging joint with motion estimation is solved by using a parametric sparse Bayesian leaning (P-SBL) method, including sparse imaging and dictionary learning. Finally, experiments are performed to confirm the effectiveness of the proposed method by using the simulated and measured data.