Passive Bistatic SAR with GNSS Transmitter and A Stationary Receiver

Space-Surface Bistatic Synthetic Aperture Radar (SS-BSAR) is a sub-class of BSAR, where the transmitter can be any satellite and the receiver could be mounted on an aircraft, a land vehicle or stationary on the ground. If stationary receiver is applied, the synthetic aperture is fully attributed to the moving of satellite. Such a kind of geometry is suited for ground deformation monitoring. Our research employs Global Navigation Satellite System (GNSS) as transmitter of Opportunity. This has many advantages. The medium Earth orbit (MEO) of GNSS satellite allows shorter satellite revisit cycles (8-9 days) than imaging radar such as Envisat (35days). Moreover, due to large number of satellites deployed (at least 4 satellites could be seen at any time), persistent monitoring of a given area can be achieved from multiple angles simultaneously. Although the potential radar performances characteristics such as power budget and resolution are outmatched by imaging radar, theoretical calculation shows that they are adequate for our purpose if long integration time is employed. This thesis presents research results on the imaging capability of SS-BSAR with GNSS and a stationary receiver. Firstly, the system is outlined and the reason of selecting GNSS as transmitter of opportunity is justified. The power budget and resolution were then analyzed. Simulation indicates that a target with Radar Cross Section (RCS) of 50 can be detected at range of 1000m if using an integration time of 5 minutes. The end to end signal processing, from received raw data leading to SSBSAR image, is described. This includes signal synchronization and image formation. The coordinate processing consists of coordinate extraction, interpolation and transformation. The newly proposed signal synchronization algorithm omits the process of unused secondary code tracking of previous algorithm and improves the efficiency by three times while achieving the same accuracy. The modified imaging algorithm integrates the synchronized signal signatures (i.e. delay, Doppler and phase) and achieves sharp focus after Back Projection (BP) operation. Four experimental data sets acquired at different imaging scenarios are used to test our system hardware and signal processing algorithms mentioned below. It can be seen from the obtained images and associated analysis that such a system has the capability of real scene imaging.