Distributed High-resolution Radar Imaging

Distributed radar system could obtain more information from targets than single radar system by its sparsely populated, three-dimensional structure. Synthetically using of the information will help to improve the quality of image effectively. The theories and methods of using the distributed data which come from various observation angles to improve image resolution were discussed in this paper, and the effects of different imaging algorithms on the imaging were especially emphasized.For distributed radar system, based on the theory that broader signal bandwidth and Doppler bandwidth lead to higher 2-D radar imaging resolution, it is reasonable to combine the signals from various observation angles at spectral domain to get synthesis data with extending bandwidth. Through the process with the synthesis data, higher resolution imaging result will be achieved.And at the same time, in a sparsely populated distributed system, large squint situation is hard to avoid while all the antennas are scanning the same target area. It causes serious distance migration. The common used Range-Doppler (RD) algorithm has some approximate calculations during the processing, which perhaps make the imaging result not satisfy the high resolution requirement.Therefore, this thesis tried to use different algorithms in spectrum extending method for distributed system to improve imaging accuracy. The RD, improved RD, Chirp Scaling (CS) and NCS algorithm were analyzed and tried in the thesis. And we could find that the different approximate processes of these algorithms cause different imaging qualities.The paper described the processing approaches and effects of using these algorithms in spectrum extending method in detail. And the simulations respectively based on the system structure of Z-direction linear, azimuth-direction linear and 2-D planar Arrangement are presented. At last, the paper summarized the characteristics of distributed system, and their influences on the requirements of choosing algorithm based on comparison of imaging results with same parameters and situations.