Sparse synthetic aperture radar imaging with optimized azimuthal aperture

Abstract

To counter the problem of acquiring and processing huge amounts of data for synthetic aperture radar (SAR) using traditional sampling techniques, a method for sparse SAR imaging with an optimized azimuthal aperture is presented. The equivalence of an azimuthal match filter and synthetic array beamforming is shown so that optimization of the azimuthal sparse aperture can be converted to optimization of synthetic array beamforming. The azimuthal sparse aperture, which is composed of a middle aperture and symmetrical bilateral apertures, can be obtained by optimization algorithms (density weighting and simulated annealing algorithms, respectively). Furthermore, sparse imaging of spectrum analysis SAR based on the optimized sparse aperture is achieved by padding zeros at null samplings and using a non-uniform Taylor window. Compared with traditional sampling, this method has the advantages of reducing the amount of sampling and alleviating the computational burden with acceptable image quality. Unlike periodic sparse sampling, the proposed method exhibits no image ghosts. The results obtained from airborne measurements demonstrate the effectiveness and superiority of the proposed method.