Abstract
Sparse signal processing has already been introduced to synthetic aperture radar (SAR), which shows potential in improving imaging performance based on raw data or a complex image. In this paper, the relationship between a raw data-based sparse SAR imaging method (RD-SIM) and a complex image-based sparse SAR imaging method (CI-SIM) is compared and analyzed in detail, which is important to select appropriate algorithms in different cases. It is found that they are equivalent when the raw data is fully sampled. Both of them can effectively suppress noise and sidelobes, and hence improve the image performance compared with a matched filtering (MF) method. In addition, the target-to-background ratio (TBR) or azimuth ambiguity-to-signal ratio (AASR) performance indicators of RD-SIM are superior to those of CI-SIM in down-sampling data-based imaging, nonuniform displace phase center sampling, and sparse SAR imaging model-based azimuth ambiguity suppression.
Highlights
IntroductionSynthetic aperture radar (SAR) is a significant remote sensing technology, which has been widely used in various fields, such as marine monitoring, topography mapping and target detection [1,2]
Synthetic aperture radar (SAR) is a significant remote sensing technology, which has been widely used in various fields, such as marine monitoring, topography mapping and target detection [1,2].In recent years, sparse signal processing theory has been introduced to SAR imaging, which shows that the sparse observed scene can be reconstructed with less sampled data [3,4,5]
Sparse signal processing theory has been introduced to SAR imaging, which shows that the sparse observed scene can be reconstructed with less sampled data [3,4,5]
Summary
Synthetic aperture radar (SAR) is a significant remote sensing technology, which has been widely used in various fields, such as marine monitoring, topography mapping and target detection [1,2]. In 2010, Patel et al proposed a more general CS-based SAR imaging model to recover observed scenes, and analyzed different azimuth sampling strategies with spotlight SAR data [8]. The method can reduce imaging time efficiently and improve image performance based on the fully sampled or down-sampled echo data for the sparse region [14,15]. A sparse signal processing-based imaging method can obtain feature-enhanced radar images [21]. The rest of this paper is organized as follows: Section 2 introduces sparse SAR imaging models based on raw data and complex images, respectively. We will analyze their equivalence at full-sampling and the inequality at under-sampling, which provides a theoretical basis for algorithm selection.
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