Study On Techniques Of Signal Processing For Cross-Track/Along-Track Interferometric Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) Interferometry is an improtant technique in remote sensing field, including cross-track interferometry (XT-InSAR or XTI) and along-track interferometry (AT-InSAR or ATI). It employs pairs of SAR images to be coherently combined, and can retrieve more information compared to a single SAR image. High quality terrain elevation model (DEM) and terrain deformation can be achieved by using the XT-InSAR, and the AT-InSAR technique can be used for ground moving target indication (GMTI) and current measurement. The wide and potential applications of SAR interferometry in military, civil and scientific researches, make SAR interferometry one of the most active fields in radar and remote sensing areas.Because the pair of SAR images for interferometry are obtained from two different view angles separated by a cross-track baseline, the interferometric phase are contaminated by noise due to baseline decorrelation, volume scattering and signal-to-noise ratio (SNR), etc. Therefore, interferometric phase filtering is one of the most improtant procedures in InSAR processing.Furthurmore, the measurement accuracy of InSAR system parameters ususlly cannot meet the practical requirement, and so it must be improved by using signal processing techniqures.In practice, the data processing speed is also an important factor to be considered. Besides the mentioned above, XTI and ATI functions are expected to be integrated into one system in order to meet different applications.The dissertation addresses the problems met in practical data processing, and presents some useful methods for XT-InSAR and AT-InSAR data processing.The main work of this dissertation are summarized as follows:1. In Chapter 2, a local gradient estimation method for obtaining enough independent and identically distributed (i.i.d.) samples in interferometric phase filtering is presented which is based on the least-squares fitting using the total interferometric fringe information. The method can combine the interferometric phases of the regions with high coherence to fit the phase gradients of the low coherence regions. Compared to the conventional local frequency estimation methods which are based on the assumption of plane surface model, the method can estimate the phase gradient of any curved surface. Thus it can suppress the phase noise greatly while maintain the interferometric fringes continuity well, combining with the conventional mean filtering.2. In Chapter 2, an interferometric phase compensation method for shadow region is presented. For a large radar look angle, the shadow phenomenon is inevitable, which will seriously degrade the accuracy and the efficiency of interferometric phase unwrapping. A pseudo-coherence is given to sharpen the edge between the regions of high coherence and low coherence, and then the inclined plane model is used to approximate the interferometric phase in shadow regions, thus facilitating phase unwrapping and improving the accuracy.3. Baseline is one of the key parameters in transforming unwrapped interferometric phase to digital elevation models in XT-InSAR. A baseline estimation method based on prior knowledge and SAR imaging parameters is presented in Chapter 3.4. Joint pixel processing for multibaseline interferometric synthetic aperture radar is robust to SAR image coregistration errors. But with the increased number of SAR images, the computational complexity is increased rapidly. The characteristic of the covariance matrix used in joint pixel processing method is analyzed in Chapter 4, and a reduced dimension method based on Lanczos iteration is presented which can reduce the dimension of the covariance matrix to slightly more than that of the signal subspace, and then the subspace fitting is utilized to achieve the terrain height estimate, thus reducing the processing time greatly.5. For a hybrid baseline InSAR system, a SAR-GMTI method is presented to detect ground moving target. The method first compensates the local terrain slope in order to obtain independent and identically distributed (i.i.d.) clutter samples, and then use the joint pixel processing method to suppress the clutter. The joint pixel processing method uses the current pixel to be detected and its surrounding pixels to jointly suppress clutter, and thus the clutter can still be effectively suppressed when the SAR images are not accurately coregistered. The phase unwrapping is then used to fit the interferometric phase of the region where ground moving targets may be located. Finally, the amplitude constant false alarm rate (CFAR) and phase CFAR are used jointly to decrease the false alarm ratio, and iteration method is also used to improve the relocation accuracy of the detected moving target.According to the classification of synthetic aperture radar interferometry, the dissertation is mainly composed of the following two parts:Part I (Chapter 2~ Chapter 4): The XT-InSAR data processing is discussed in Part I including the method for improving the interferogram quality, the local knowledge based baseline estimation method and the reduced-dimension method for joint-pixel multi-baseline InSAR processing.Part II ( Chapter 5): Gound moving target detection using along track baseline in AT-InSAR is analyzed. The content mainly includes multi-channel SAR-GMTI method with along-track baseline and SAR-GMIT method in hybrid along-/cross- track baseline InSAR formation.