Research On Key Technologies Of Ground-based Interferometric Radar Measurement

Ground-based Synthetic Aperture Radar Interferometry(GB-InSAR)is a very effective surface deformation monitoring technology developed in the past two decades,and is especially suitable for continuous monitoring of surface deformation in a small area with danger.Ground Based Synthetic Aperture Radar(GB-SAR)has flexible placement and flexible data collection time.It can monitor all-day and all-weather,has a wide range of applications in landslides,slope deformation monitoring in mining areas,glacier monitoring,building deformation monitoring,dump site safety monitoring and other fields.With the further improvement of the resolution of the GB-SAR system in the future and the popularization of multi-polarization and multi-band technology,it will support social and economic development in more fields.GB-InSAR data has the characteristics of high spatial resolution,high time resolution,large amount of data,and obvious influence of atmospheric delay.The current research on its statistical properties and data processing is not perfect,which limits its scope of application.Aiming at the key technical issues in ground-based interferometric radar measurement,and learning from spaceborne SAR technology,this paper systematically studies the statistical distribution law of GB-InSAR data,the method of small time baseline set,the PS point selection method suitable for GB-InSAR,and the weakening Atmospheric delay influence methods,digital elevation model(Digital Elevation Model,DEM)generation and accuracy factors,the correlation between elevation and atmospheric delay influence,adaptive Kalman filter technology analysis of deformation monitoring sequence data,etc.,GB-InSAR Technology provides a new way to obtain high-precision deformation.(1)The statistical distribution of ground-based synthetic aperture radar data is studied:the statistical distribution of the real part,the statistical distribution of the imaginary part,the statistical distribution of amplitude,the statistical distribution of phase,and the influence of atmospheric errors and deformation on the distribution.Using the example data,it is concluded that the PS point interference phase distribution without deformation and atmospheric influence is very close to the Laplacian distribution.(2)Propose a small time baseline set method suitable for ground-based synthetic aperture radar continuous monitoring data processing to limit the impact of atmospheric delay,and improve the traditional complex data coherence coefficient as a reference index for dividing subsets to improve the atmosphere The phase coherence coefficient,which is more sensitive to delay effects,is used as the reference index for dividing the subsets.According to the short time interval of continuous monitoring data,it is proposed to use gross error detection to solve the time dimension of the interference sequence of the single main image in the subset.Entanglement,and use example data to verify the practicability of this method.(3)Analyze the applicability of traditional PS point identification methods to ground-based synthetic aperture radar continuous monitoring data,discuss the advantages and disadvantages of these methods,and propose a new selection of stable scattering characteristics based on the short time interval of continuous monitoring data Point method—the interference phase limit error elimination method of adjacent scenes,and its practicality is verified with example data.(4)Analyze the pros and cons of the existing ground-based synthetic aperture radar data to remove the effects of atmospheric delay,and improve the stable point distance function fitting method suitable for dangerous survey areas,that is,based on the fact that in a small local area,The influence of atmospheric delay in different azimuths is very close to the characteristics of the change in the distance.A segmented stable point distance function fitting method is proposed to remove the influence of atmospheric delay,and the practical data is verified to a certain extent.(5)A detailed analysis of the vertical baseline model of DEM generated by ground-based synthetic aperture radar and its accuracy influencing factors are carried out.DEM is generated with example data,and combined with the changes in the spatial domain of the atmospheric delay in the survey area,the influence of atmospheric delay in the spatial domain is proposed.The change has a certain correlation with the change of the survey area elevation.(6)Using adaptive Kalman filtering technology to analyze the slope deformation sequence data obtained in Chapter 3,and predict and analyze the deformation trend of the deformation body in the short term.