Research On Imaging Method Of Synthetic Aperture Interferometric Radiometry Based On Virtual Antenna Arrays

Synthetic aperture interferometric radiometer(SAIR)can synthesis a large aperture by sparsely arranging a number of small aperture antennas to achieve high spatial resolution imaging with a wide instantaneous field-of-view(FOV)and without requiring very large and massive mechanical scanning antenna.Because of these advantages,since SAIR is introduced from radio astronomy to the Earth observation,the technique and application of SAIR are developing very fast,and increasing interests for spaceborn missions are being observed in the last decades,especially for the teams from Europe,USA and China.However,it has been shown that radio frequency interference(RFI)sources present in many microwave radiometry bands and many areas of the world due to the presence of wireless device,which dramatically degrades the performance for the retrieve of geophysical parameters.Compared with real aperture radiometry,RFI is even more serious for SAIR because of the large FOV and the Gibbs-like contamination in the brightness temperature(BT)images from the limited spatial frequency coverage of a SAIR instrument.In this dissertation,in order to reduce the severe degradation effects of RFIs,based on the further analysis for the relationship between the interferometric radiometry and the general array processing,the imaging method of synthetic aperture interferometric radiometry based on virtual antenna arrays is proposed.The main contents of this dissertation are given as follows.(1)After briefly introducing the basis concept of microwave radiometry,and formulating the principles of synthetic aperture interferometric radiometry,further analysis on the unified mathematical framework between the SAIR and the general array processing are presented from the aspects of the system model and equivalent array factor.It is shown that digital beamforming of spatial match filtering based on the general array processing model is equivalent to the discrete Fourier transform of measured visibilities in SAIR with a certain window function,which is the autocorrelation function for the binary series determined by the antenna array placement.Then,the results of SAIR imaging by directly using digital beamforming will degenerate due to the sparse array configuration of SAIR.(2)An imaging method with array factor synthesis in synthetic aperture interferometric radiometers is proposed,which can efficiently control the characteristics of the equivalent array factor of SAIR,such as null position and depth,sidelobe level,mainlobe efficiency and so on.First,the theoretical formulation is established that the SAIR array is equivalent to a virtual antenna array.Then,two types of optimization problems of the array factor synthesis are presented: weight vector norm minimization and sidelobe level minimization.Finally,it is shown that the presented method can be viewed as a specific window with a conjugate symmetry property,and a resulting weight vector scaling is presented to correct the inverse BT.In particular,it is also shown that,in some special case,the presented method is equivalent to the built-in method proposed by A.Camps recently and the conventional inverse discrete Fourier transform(IDFT)method.Simulation results and real SMOS data experiments validate the effectiveness of the presented method.In addition,RFI mitigation results for SMOS applications show that,for the approach of array factor synthesis of weight vector norm minimization,weak and moderate RFIs can be mitigated very well,but it is not enough effective for strong RFIs due to residual calibration errors.On the other hand,for strong RFIs,at the cost of spatial resolution degradation,result shows the approach of sidelobe level minimization can be effective to mitigate strong RFIs.(3)A novel RFI localization approach using covariance matrix augmentation in SAIR is proposed.The proposed approach utilizes the property of the sparse array configuration which is commonly used in SAIR,where the sparse array can be viewed as a virtual filled array with much larger number of antenna elements.The virtual filled arrays of several typical sparse SAIR array geometries(such as,U-,T-,Y-and hexagonal array)are discussed,the construction of the augmented covariance matrix for the virtual filled array is presented,and a typical Direction-of-Arrival estimation approach using MUltiple SIgnal Classification(MUSIC)algorithm based on the augmented covariance matrix is employed for RFI localization.The proposed approach can be applied in SAIR with a sparse array configuration,such as the European Space Agency(ESA)Soil Moisture and Ocean Salinity(SMOS)mission.Results on real SMOS data show that,compared with the classical MUSIC algorithm,the presented approach has an improved performance of RFI localization with comparable accuracy,such as improved spatial resolution,lower sidelobes and larger identifiable number of RFI sources.In addition,for the natural scene imaging application,the digital beamforming method based on augmented covariance matrix is also presented for SAIR.Experiment results of Huazhong University of Science and Technology Aperture Synthesis Radiometer(HUST-ASR)show that,compared with the digital beamforming based on the direct covariance matrix,the presented approach shows a significantly improved imaging performance with respect to the spatial resolution.(4)Utilizing the sparse property of RFI in the observed scene,a high resolution RFI localization method based on compressive sensing is presented in SAIR.The proposed approach is based on the model that SAIR can be understood as a virtual array with a virtual antenna element located in each baseline.When the scene brightness temperature is viewed as a background noise,the RFI sources that we desire to recover are sparse in the spatial domain.Based on the fact,the ?1-norm minimization and reweighted ?1-norm minimization are proposed to enforce the sparsity of the desired RFI sources,and the approach for the choosing of the regularization parameter are also presented.Numerical results show the presented method can achieve super-resolution for RFI localization even when there are some missing data due to correlator or receiver failures.