The phase error estimated by phase gradient autofocus(PGA) is not based on a finite order polynomial mode, so PGA has a good autofocus property for arbitrary order phase error and is fit for high resolution airborne S...The phase error estimated by phase gradient autofocus(PGA) is not based on a finite order polynomial mode, so PGA has a good autofocus property for arbitrary order phase error and is fit for high resolution airborne SAR. But PGA has two shortcomings: first, it has a worse estimation property for fast changing phase error; second, there exists a section of linear phase in the phase error estimated by this algorithm. This paper introduces the idea of rank one phase estimate (ROPE) autofocus technique, and improves PGA. The improved PGA(IPGA) can successfully overcome both these shortcomings of PGA.展开更多
A new approach to phase averaging in phase gradient algorithm (PGA) is proposed, which is based on the fundamental fact that the information of translational phase error is widely contained in every defocused scattere...A new approach to phase averaging in phase gradient algorithm (PGA) is proposed, which is based on the fundamental fact that the information of translational phase error is widely contained in every defocused scatterer in ISAR image. The new approach aims to choose strong scatterers for error phase averaging with a threshold rather than just simply to pick out the strongest point in each range cell, which is not necessarily real strong scatterers if in some range cells consists more than one strong scatterer and whereas in other range cells no scatterer at all. The results of processing real data are presented to confirm the validity of the proposed approach.展开更多
MOtion COmpensation(MOCO) is an essential step in high resolution airborne Synthetic Aperture Radar(SAR) imaging. Generally, a reference altitude level is assumed and external Digital Elevation Model(DEM) is required ...MOtion COmpensation(MOCO) is an essential step in high resolution airborne Synthetic Aperture Radar(SAR) imaging. Generally, a reference altitude level is assumed and external Digital Elevation Model(DEM) is required for the scene topography heavily varied. To overcome the shortcoming, we propose a MOCO method based on Phase Gradient Autofocus(PGA) which can obtain well focused images without DEM. In the implementation, we first compensate the normal range-invariant term. Then the data are divided into strips in range-compressed domain and PGA is applied to each substrip to extract the phase errors. Finally, the phase error surface is obtained using interpolation and then compensated. Real airborne SAR data of a UAV-SAR system experiments and comparisons demonstrate the validity and effectiveness of the proposed algorithm. The results show that our algorithm is effective.展开更多
文摘The phase error estimated by phase gradient autofocus(PGA) is not based on a finite order polynomial mode, so PGA has a good autofocus property for arbitrary order phase error and is fit for high resolution airborne SAR. But PGA has two shortcomings: first, it has a worse estimation property for fast changing phase error; second, there exists a section of linear phase in the phase error estimated by this algorithm. This paper introduces the idea of rank one phase estimate (ROPE) autofocus technique, and improves PGA. The improved PGA(IPGA) can successfully overcome both these shortcomings of PGA.
文摘A new approach to phase averaging in phase gradient algorithm (PGA) is proposed, which is based on the fundamental fact that the information of translational phase error is widely contained in every defocused scatterer in ISAR image. The new approach aims to choose strong scatterers for error phase averaging with a threshold rather than just simply to pick out the strongest point in each range cell, which is not necessarily real strong scatterers if in some range cells consists more than one strong scatterer and whereas in other range cells no scatterer at all. The results of processing real data are presented to confirm the validity of the proposed approach.
文摘MOtion COmpensation(MOCO) is an essential step in high resolution airborne Synthetic Aperture Radar(SAR) imaging. Generally, a reference altitude level is assumed and external Digital Elevation Model(DEM) is required for the scene topography heavily varied. To overcome the shortcoming, we propose a MOCO method based on Phase Gradient Autofocus(PGA) which can obtain well focused images without DEM. In the implementation, we first compensate the normal range-invariant term. Then the data are divided into strips in range-compressed domain and PGA is applied to each substrip to extract the phase errors. Finally, the phase error surface is obtained using interpolation and then compensated. Real airborne SAR data of a UAV-SAR system experiments and comparisons demonstrate the validity and effectiveness of the proposed algorithm. The results show that our algorithm is effective.
