Study On Key Techniques Of Signal Processing For Interferometric Synthetic Aperture Radar

Synthetic aperture radar interferometry (InSAR) is a new radar imaging technique derived from synthetic aperture radar (SAR). Conventional SAR measurement has the fine resolution of the range and azimuth direction obtained by the techniques of pulse compression and matched filtering, respectively, and thus can locate targets accurately in a two-dimensional coordinate system. Based on SAR systems, InSAR needs several observations over the same scene at different time or along different tracks, together with the knowledge of the interferometer geometry and phase differences between each pair of the received signals that can be converted into a digital elevation map or topography-changing map. The wide and the potential applications of InSAR in many areas such as military, civil and scientific researches, make InSAR one of the most active fields in radar and remote sensing.As is well known, image coregistration, interferometric phase noise filtering, and phase unwrapping are three major processing procedures of InSAR, and the three procedures are cascaded in conventional InSAR processing flow. However, the cascaded processing flow is not optimal due to the fact that the current processing stage suffers from the output of the preceding stage. For example, if the information is impaired due to the image misregistration, the phase noise filtering and phase unwrapping steps cannot recover the lost information.Aiming at these problems above, this dissertation discusses the processing procedures of interferometric SAR deeply, with emphasis on the key techniques of signal processing such as interferometric phase estimation (or noise filtering) and interferometric phase unwrapping in the presence of coregistration errors. The main works of the dissertation can be summarized as follows.1. We propose a new method based on the model of true steering vector to estimate the terrain interferometric phase in the presence of large coregistration errors. Benefiting from the true steering vector in the presence of large coregistration errors the method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. The key steps of this method are outlined as follows:Estimate the covariance matrix by using the joint data vector after the coarse coregistration.Determine the true steering vector in the presence of large coregistration error according to the joint data vector.Estimate the InSAR interferometric phase by beamforming and Capon methods with the determined true steering vector. For a pair of SAR images that are coregistered inaccurately, the proposed method can auto-coregister them and accurately estimate the corresponding terrain interferometric phase.A fast algorithm is developed to implement this method, which can significantly reduce the computational burden. The effectiveness of the method is verified via simulated data and real data.2. We propose a method based on minimum mean squared error (MMSE) criterion to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. In this method, the cross-correlation coefficient vector with large coregistration error is given firstly, and then the cost function under the MMSE criterion is used to estimate the InSAR interferometric phase. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. The key steps of this method are summarized as follows:Estimate the cross-correlation coefficient vector according to the data vector after the coarse coregistration.Determine the cost function under the MMSE criterion using the cross-correlation coefficient vector.Estimate the InSAR interferometric phase by using the cost function. For a pair of SAR images that are not coregistered accurately, the proposed method can auto-coregister them and accurately estimate the corresponding terrain interferometric phase. A fast algorithm is developed to implement the method, which can significantly reduce the computational burden. The effectiveness of the method is verified via simulated data and real data.3. Based on the data vector weighted by correlation coefficients, we propose a phase unwrapping method for multibaseline InSAR systems which does not need to calculate the dimension of the noise subspace before estimating the absolute interferometric phase. In this method, the model of the joint single pixel is used and the data vector weighted by correlation coefficients is formed. In this way, it can improve the coherence of the other element of the data vector with respect to the reference element of it. The method can carry out image coregistration, interferometric phase noise filtering and phase unwrapping simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the absolute interferometric phase (phase unwrapping) even when the coregistration error reaches one pixel.