Sparsity-Driven SAR Imaging for Highly Maneuvering Ground Target by the Combination of Time-Frequency Analysis and Parametric Bayesian Learning

Abstract

In this paper, a well-focused synthetic aperture radar (SAR) image for highly maneuvering ground target is formed. For high-resolution SAR imaging, the phase modulation from the maneuverer's high-order movements severely degrades the focusing quality of the target image, if the conventional SAR imaging algorithm under the constant target velocity assumption is used. To deal with this problem, a new SAR ground moving target imaging (GMTIm) algorithm is presented with a two-step strategy to obtain a high-resolution maneuvering target image with highly focused responses. Pseudo Wigner-Ville distribution is first employed to access and compensate for the bulk of the high-order phase. Then, to further enhance the target image quality, the SAR-GMTIm problem is solved by sparse Bayesian learning (SBL), where an accurate phase autofocusing is incorporated for the compensation of the residual high-order phase. A novel time-frequency representation, known as Lv's distribution, is adopted to determine the parametric dictionary used in the SBL processing. To accommodate the possible multiple-target imaging scenario, the intended SAR-GMTIm algorithm is developed in a coarse-to-fine compensation procedure. Finally, both simulated data and real Gotcha data are applied to validate the effectiveness and superiority of the proposed SAR imaging algorithm for ground maneuvering targets.