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SCIENCE CHINA Information Sciences, Volume 59, Issue 8: 082307(2016) https://doi.org/10.1007/s11432-015-5446-z

High-precision deformation monitoring algorithm for GBSAR system: rail determination phase error compensation

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  • ReceivedJun 4, 2015
  • AcceptedJul 17, 2015
  • PublishedFeb 17, 2016

Abstract

In recent years, the repeat-pass GBSAR (ground based synthetic aperture radar) system has demonstrated its capacity to acquire deformation. Nevertheless, in a variety of applications, it needs to measure the deformation with the precision up to 0.1 mm, which could not be reached by utilizing the traditional PS (permanent scatterer) algorithm in most cases. Generally, one of the main reasons could be summarized into the phase error caused by the rail determination error, because the precision of rail determination might degrade during long working hours. However, the traditional PS algorithm could not compensate for the phase error caused by the rail determination error. In order to solve the problems, we modify the conventional PS algorithm. Firstly, we deduced the transformation relationship between the rail determination error and its corresponding interferometric phase error. Then, the phase errors caused by the atmosphere and the rail determination error were jointly compensated. The experimental data, which were obtained in Fangshan District in Beijing (China), were used to test and verify the performance of the new algorithm. After the comparison between the results processed by the new algorithm and those processed by the traditional algorithm, the proposed method demonstrated its ability to obtain high-precision deformation.


Funded by

111 Project of China(B14010)

Chang Jiang Scholars Program(T2012122)

National Natural Science Foundation of China(61471038)

National Natural Science Foundation of China(61427802)

National Natural Science Foundation of China(61120106004)

National Natural Science Foundation of China(61225005)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61120106004, 61225005, 61471038 and 61427802), Chang Jiang Scholars Program (Grant No. T2012122), and 111 Project of China (Grant No. B14010).


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