Terrain Height Estimations Of MIMO InSAR

Synthetic aperture radar Interferometry (InSAR) is a powerful remote sensing technique, which has been exploited for the generation of digital elevation model with high resolutions and has found many applications in the earth system science study. The accuracy and robustness of interferometric phase unwrapping of the traditionally single baseline InSAR suffers severely from the interferometric phase noises. And it is difficult for the single baseline InSAR to deal with the highly sloping, discontinuous and Layover scene. Recently, the multi-baseline/multi-frequency InSAR system, which would be referred as multi-channel InSAR in the following, has been widely investigated for its abilities to restore a unique solution to the terrain height and resolve the terrain mapping problem of the highly sloping, discontinuous and Layover scene. The newly emergent MIMO radar concept, depending on its ability to virtually extend the sensor array, provides a feasible way to improve the performance of multi-channel InSAR without extending the number of real sensors or frequencies. This dissertation would mainly focuses on the application of MIMO radar concept to the multi-channel InSAR. The research of this dissertation is summarized below:1. A new joint processing method is proposed, which carries out the height estimation through the joint covariance matrix fitting. The eigen-spectrum of the joint pixel vector is analyzed firstly, which shows that the joint noise subspace would exist only under some specific conditions. The original joint processing method based on joint subspace projection would suffer from the loss of interferometric information since, in this case, the absent joint noise subspace would be replace by the eigen-subspace which also contains some interferometric information. However, through the joint covariance matrix fitting, all the interferometric information embedded in the joint pixel vector would be exploited for the height estimation. Further more, the sample joint covariance matrix is tapered by the coherence matrix, which improves the robustness of the proposed method in the case of limited samples available.2. Two MIMO InSAR modes are proposed, which are based on the frequency diversity and the waveform diversity, respectively. The data models for these two modes are presented. And the performance of these two modes are analyzed theoretically, which indicates that MIMO InSAR is able to identify the laid over ground patches more than its real sensors and provided a more accurate estimation of the terrain profile than that of its SIMO counterpart. Hence, MIMO InSAR would exhibit a better identifiability in the areas of complex terrain profile such as mountains, buildings and other complex man made objects, especially when the number of real sensors is small.3. Since the orthogonality of the available orthogonal waveforms at present could not satisfy the requirement of the available SAR imaging technique, two substitutive MIMO InSAR modes based on spatial/temporal encoding are proposed, which remain the advantages of the waveform diversity based MIMO InSAR mode. Under the temporal encoding mode, the MIMO channels are obtained through transmitting signals alternately from the sensors. And the ScanSAR technique is employed to enlarge the swath width under such mode. While, under the spatial encoding mode, the MIMO channels are obtained through spreading the footprints of the mainlobes of the transmitting beams along the observing strip. And the antenna of each sensor is divided into multiple sub-antennae along the azimuth direction for the spatial filtering on receive, which makes it possible to avoid doppler ambiguity without increasing the PRF. The orthogonality of the transmitting signals is no more necessary under these two modes. Thus, the linear frequency modulated signal could be employed as the transmitting signal of every sensor, which would make it possible to complete the imaging processing via the ordinary SAR imaging algorithms.4. A computationally efficient method employing ESPRIT and Kalman filtering is proposed for the direction finding of MIMO radar. The property that the virtual array of MIMO radar is equivalent to multiple virtual subarrays with the same geometry but different displacements is exploited to provide multiple direction estimates for each target. And the multiple estimates of each target are fused through Kalman filtering to improve the accuracy of direction estimation. This method has no specific requirement on the geometry configuration of the MIMO array and can resolve the parameter estimation problem of array in the near-field, which make it applicable to the height estimation problem of MIMO InSAR. Therefore, computationally efficient method is presented for the height estimation of MIMO InSAR based on the basic concept of the method mentioned above. Furthermore, the impact of multiplicative noise on this method is analyzed theoretically when it is applied to the height estimation problem of MIMO InSAR.