A Deconvolution-Based Grating Lobes Reduction for Low-Oversampled Staggered SAR Image

The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method.