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Three dimensional(3D) deformation can be obtained by using differential interferometric synthetic aperture radar(D-In SAR) technique with the cross-heading tracks data of low earth orbit(LEO) SAR. However,this method has drawbacks of the low temporal sampling rate and the limited area and accuracy for 3D deformation retrieval. To address the aforementioned problems, by virtue of a geosynchronous(GEO) SAR platform,this paper firstly demonstrates the expressions of 3D deformation and the corresponding errors in GEO SAR multi-angle processing. An optimal multi-angle data selection method based on minimizing position dilution of precision(PDOP) is proposed to obtain a good 3D deformation retrieval accuracy. Moreover, neural network is utilized for analyzing the accuracy of the retrieved 3D deformation under different orbit configurations and geo-locations. Finally, the proposed methods and the theoretical analysis are verified by simulation experiments.A 3D deformation retrieval accuracy of the order of centimeter-level or even millimeter-level can be obtained by using the selected optimal multi-angle data.
However, this method has drawbacks of the low temporal sampling rate and the limited area and accuracy for 3D deformation retrieval. To address the previous problems, by virtue of a geosynchronous (GEO) SAR platform, this paper first demonstrates the expressions of 3D deformation and the corresponding errors in GEO SAR multi-angle processing. An optimal multi-angle data selection method based on minimizing position dilution of precision (PDOP) is proposed to obtain a good 3D deformation retrieval accuracy. Moreover, neural network is utilized for analyzing the accuracy of the retrieved 3D deformation under different orbit configurations and geo Finally, the proposed methods and the theoretical analysis are verified by simulation experiments. A 3D deformation retrieval accuracy o f the order of centimeter-level or even millimeter-level can be obtained by using the selected optimal multi-angle data.