A High-Resolution Imaging Method for Strip-Map SAR With Missing Data

Abstract

Due to the long aperture, the high-resolution imaging for strip-map SAR with missing data is a challenge, in which the range migration correction and phase error correction are challenging. In this paper, a high-resolution imaging method of this type of data based on compressed sensing (CS) is proposed, which divides the strip-map data into several sub-apertures restored by CS and recombined to the strip-map data. The basis matrix and the measurement matrix for CS are deduced. The sub-aperture data is autofocused by the Projection Approximation Subspace Tracking (PAST) algorithm to meet the sparse requirement for the reconstructed image and the intact phase error data is restored by CS in order to combine the subapertures. A high-resolution image of the restored data can be obtained by conventional imaging method which performs range migration and autofocus. INDEX TERMS Synthetic aperture radar, compressed sensing, PAST algorithm, missing data, highresolution imaging.

Processing of Incomplete Multichannel SAR Data for HRWS Imaging

“…It is shown that this kind of problem can be solved using the compressive sensing (CS) technique [19] if the SAR data is sparse in a specific domain [20]- [22]. However, the reconstruction methods based on the CS technique are suitable for situations where the effect of range migration can be assumed negligible, which is not evident in the stripmap SAR mode [23]. Also, the computational complexity in solving CS problems dramatically increases when the size of the SAR data is huge.…”