Sparsity-Driven Inverse Synthetic Aperture Radar Imaging Using Accelerated Meta-Heuristic Optimization

Abstract

When the inverse synthetic aperture radar (ISAR) system have sparse aperture (SA) dataset, which are non-uniform and less than Nyquist sampling rate along the cross-range axis, it can significantly blur the ISAR image of target having complex motion. To overcome this, we propose an improved ISAR imaging method based on dictionary estimation using parametric signal reconstruction (DEPSR) using accelerated meta-heuristic optimization (MHO). The proposed method reconstructs a motion-compensated and full-aperture (FA) dataset from a sparse-aperture (SA) dataset using MHO, which can be accelerated by a graphics processing unit (GPU). After then, we can obtain the motion-compensated FA dataset and by using this, we can exploit the conventional range-Doppler (R-D) processing. Simulation and real-data-based ISAR imaging results validate the superiority of the proposed method in terms of image quality in comparison with conventional methods, such as simple R-D processing, basis pursuit denoising, and DEPSR. Particularly, the proposed method exhibits a significantly lower computational burden than that of the traditional DEPSR approach.