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Remote Sens. 2019, 11(3), 340;

Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing

National Lab of Radar Signal Processing and Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi’an 710071, China
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China
Authors to whom correspondence should be addressed.
Received: 12 January 2019 / Revised: 31 January 2019 / Accepted: 4 February 2019 / Published: 8 February 2019
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
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Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift algorithm (2D-SVMDA) is developed to provide robust autofocusing performance for UAV SAR imagery. This proposed algorithm combines two enhanced map-drift kernels. On the one hand, based on the azimuth-dependent phase correction, a novel azimuth-variant map-drift algorithm (AVMDA) is established to model the residual phase error as a linear function in the azimuth direction. Then the model coefficients are efficiently estimated by a quadratic Newton optimization with modified maximum cross-correlation. On the other hand, by concatenating the existing range-dependent map-drift algorithm (RDMDA) and the proposed AVMDA in this paper, a phase autofocus procedure of 2D-SVMDA is finally established. The proposed 2D-SVMDA can handle spatial-variance problems induced by strong phase errors. Simulated and real measured data are employed to demonstrate that the proposed algorithm compensates both the range- and azimuth-variant phase errors effectively. View Full-Text
Keywords: synthetic aperture radar (SAR); motion compensation (MOCO); azimuth-variant map-drift algorithm (AVMDA); two-dimensional spatial-variant map-drift algorithm (2D-SVMDA); autofocus synthetic aperture radar (SAR); motion compensation (MOCO); azimuth-variant map-drift algorithm (AVMDA); two-dimensional spatial-variant map-drift algorithm (2D-SVMDA); autofocus

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wang, G.; Zhang, M.; Huang, Y.; Zhang, L.; Wang, F. Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing. Remote Sens. 2019, 11, 340.

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