Abstract
This article presents a new scheme called 2-D mixed compressive sensing back-projection (CS-BP-2D), for synthetic aperture radar (SAR) imaging on a geocoded grid, in a single measurement vector frame. The back-projection linear operator is derived in matrix form and a patched-based approach is proposed for reducing the dimensions of the dictionary. Spatial compressibility of the radar image is exploited by constructing the sparsity basis using the back-projection focusing framework and fast solving the reconstruction problem through the orthogonal matching pursuit algorithm. An artifact reduction filter inspired by the synthetic point spread function is used in postprocessing. The results are validated for simulated and real-world SAR data. Sentinel-1 C-band raw data in both monostatic and space-borne transmitter/stationary receiver bistatic configurations are tested. We show that CS-BP-2D can focus both monostatic and bistatic SAR images, using fewer measurements than the classical approach, while preserving the amplitude, the phase, and the position of the targets. Furthermore, the SAR image quality is enhanced and also the storage burden is reduced by storing only the recovered complex-valued points and their corresponding locations.
Highlights
S YNTHETIC aperture radar (SAR) imaging is a powerful technique for direct or indirect measurements of physical parameters characterizing the illuminated scene, capable of collecting day-and-night, all-weather data
They propose an improved version for bistatic synthetic aperture radar (SAR) focusing called fast bistatic fast factorized BP based on SAR sub-aperture processing sped-up by the fast Fourier transform algorithm
The matrices associated with BP processing steps are denoted as follows: matched filter matrix (MFM), grid interpolation matrix (GIM) and azimuth summation matrix (ASM)
Summary
S YNTHETIC aperture radar (SAR) imaging is a powerful technique for direct or indirect measurements of physical parameters characterizing the illuminated scene, capable of collecting day-and-night, all-weather data. The Matched filter [1], [2], the Range–Doppler [22] or the Chirp-Scaling [3] and ω − k SAR image formation algorithms inspired CS-based frameworks for generating high-quality images, while, at the same time, decreasing the amount of stored data. This article introduces a combined CS-BP SAR imaging framework called CS-BP-2D for a user-defined grid, which manages to discard raw data while preserving the magnitude and the phase of the SAR image.
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