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

Huajun Duan,Daiyin Zhu,Tianshun Xiang

doi:10.1109/access.2019.2949657

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 sub-apertures. A high-resolution image of the restored data can be obtained by conventional imaging method which performs range migration and autofocus.

Highlights

Synthetic Aperture Radar (SAR) can obtain high-resolution images in day/night and all-weather, so it has been widely used in both the military and the civil application
This paper proposes a high-resolution imaging method for strip-map SAR with missing data based on compressed sensing
The strip-map missing data is divided into several sub-apertures in order to reduce the data amount and the computational complexity, the intact sub-aperture data is recovered with compressed sensing (CS)

Summary

INTRODUCTION

Synthetic Aperture Radar (SAR) can obtain high-resolution images in day/night and all-weather, so it has been widely used in both the military and the civil application. H. Duan et al.: High-Resolution Imaging Method for Strip-Map SAR With Missing Data mosaicking. Literature [22] studied the CS processing method of sparse aperture SAR for strip-map mode, which was based on the R-D imaging algorithm. This method just corrected the range walk without range curvature correction and phase error compensation, leading to degrade image resolution. The range migration of the missing data is corrected, and a high-resolution image is obtained by processing the restored data with the conventional imaging method [23] and autofocus [24]. Where H represents the height of the plane above the topography, R0 is the range to the reference point, and R is the range bin size

SUB-APERTURE SEGMENTATION

SUB-APERTURE RECONSTRUCTION BASED ON CS

PHASE ERROR SIGNAL RECOVERY

THE MEASURED DATA PROCESSING RESULTS AND ANALYSIS

CONCLUSION