Study Of Three-dimentional Synthetic Aperture Radar Tomography Imaging Technology

In recent years, the technique of three-dimensional Synthetic Aperture Radar (SAR) imaging has been becoming one of the focuses in RADAR field because of such specific advantages as all-time, all-weather, high-resolution, electromagnetic penetration, no projection blurring and broad application prospects in military reconnaissance, earth remote sensing, marine research, environmental protection and disaster monitoring. Based on the tomography, SAR tomography (TomoSAR) can generate high resolution three-dimensional imagery by combining multiple SAR images acquired on flight paths slightly separated in the elevation direction. In the field of radar imaging, TomoSAR has important research value because it have a true three-dimensional imaging capability, does not require complex flight trajectory controls, additional positioning accuracy requirements. However, many problems and challenges are needed to be resolved for perfecting the TomoSAR.This dissertation carries out research on TomoSAR with focuses on the key techniques such as systems theory, SAR images co-registration, suppressing noise interference, imaging with sparse baselines and airborne extended application. The main work and contributions are presented as follows:1) To some extent, TomoSAR theory and system are supplemented. With the view of tomography, the characters of radar signal are analyzed firstly. Secondly, the TomoSAR three-dimensional imaging principle is presented and the performances of TomoSAR are deduced. Then the effects from the factors of system are discussed such as the resolution of SAR images, the errors in track positioning, the precision of SAR image co-registration, noise, the number and distribution of tracks et al. Fourthly, the solutions to some key problems are studied and presented.2) A novel feature-based method for SAR images co-registration is proposed. In this method, the co-registration is formulated as a functional optimization problem based on level set firstly, then the level set function and the registration function are interleaving evolved iteratively to register the images. By this method, the speckle effect on feature recognition is suppressed and the errors occurred in recognition is avoided to impact registration simultaneously.3) A new imaging approach is proposed for TomoSAR based on higher-order statistics. By formulating the imaging to harmonic retrieval problems based on higher-order cummulants, this approach can deal with colored (and whiten) Gaussian noise and symmetric (and non-symmetric) non-Gaussian noise. The TomoSAR is extended to more complex situations.4) To deal with TomoSAR imaging with sparse baselines, an approach is proposed based on sparse signal representation. Firstly, the imaging is formulated as a signal sparse representation problem through the hypothesis that the signal in elevation direction are from limited scattering centers. Then the imagery is obtained by solving the representation problem through sparse Bayesian learning algorithm. With this approach, the TomoSAR can obtain imagery from more flexible SAR tracks both number and distribution.5) An airborne TomoSAR system configuration with multi-antennas is presented where all the antennas are distributed along the wings, one antenna transmits and receives electromagnetic waves, and the others are receivers only. With this configuration, the TomoSAR can work with fewer flights, less flight risk, larger flight positioning precision.In this dissertation, some practical issues of three-dimensional SAR tomography imaging are mainly discussed. To a certain extent, the work presented in this dissertation enriches the TomoSAR systems and theories. With the devolopement of TomoSAR, it will be applied in various fields.