Coherent Processing for ISAR imaging with Sparse Apertures

Inhalt:
Compressive sensing (CS) has been successfully applied in inverse synthetic aperture radar (ISAR) imagery. By exploiting its inherent sparsity, high resolution ISAR image can be generated with sparse measurements, such as sparse frequency bands and apertures. In this paper, we derive an imaging and error correction scheme for Sparse Aperture (SA) ISAR imagery. Based on Bayesian Compressive Sensing (BCS), SA ISAR imaging is converted into a classical sparsity-driven optimization problem, wherein the weighting parameter can be calculated from the statistical estimation. The coherent processing for phase error correction is demonstrated in detail. Experiments with real data validate the presented scheme