Study On Moving Target Detection And Imaging For Bistatic SAR

The moving targets detection and imaging methods of bistatic SAR has been studied in this thesis. An FrFT-based algorithm and banks of CS filter based algorithms including BSC-BCSA and BMC-BCSA for bistatic SAR moving targets detection and imaging are proposed. All of these algorithms can achieve detection and imaging of moving targets. The main contents of this thesis can be summarized as follows:1. The basic model of moving targets detection and imaging is investigated. On the basis of analyzing this model, the effect of target motion on moving targets imaging is illustrated, and the principle of two kinds of methods for moving targets detection and imaging is introduced.2. An FrFT-based algorithm for bistatic SAR moving targets detection and imaging is presented. The model of bistatic SAR moving targets detection and imaging is established, and the FrFT is used to focus the azimuth echo signal of the moving target. Moreover, the interferometry technique is applied to estimate the motion parameters of the target. Thus, the detection and imaging of moving targets can be achieved. The simulation results validate the effectiveness of the FrFT-based algorithm.3. A BSC-BCSA algorithm based on banks of CS filter for bistatic SAR moving targets detection and imaging is proposed. The corresponding bistatic SAR moving targets detection and imaging model of single channel is established. L banks of filters based on the theory of Chirp-Scaling algorithm are designed to achieve moving targets detection and imaging. The validity of the algorithm would be proved by the simulation results.4. A BMC-BCSA algorithm based on banks of CS filter for bistatic SAR moving targets detection and imaging is proposed. In order to reduce the impact of strong clutter on the performance of moving target detection and imaging, multi-channel SAR is introduced, and STAP algorithm is applied to clutter cancellation. Compared with the single channel algorithm, this algorithm can achieve detection and imaging in low SCR conditions. Simulation results illustrate that this algorithm can generate the good detection and focusing performance.