A Coherence-Oriented Fast Time-Domain Algorithm for UAV Swarm SAR Imaging With Trajectory Difference Correction and Data-Driven MOCO

By equipping the synthetic aperture radar (SAR) sensors on multiple unmanned aerial vehicles (UAVs) to form a UAV swarm (UAVS) and operate collaboratively, UAVS-SAR presents the significant advantages of rapid echoes acquisition, high imaging frame rate as well as high system survivability for advanced SAR applications. However, due to the flexible trajectories as well as the distributed configuration, the problem of the spectrum blurring in the UAVS-SAR is more complicated than that of the conventional monostatic/bistatic SAR configurations, which makes the current fast time domain algorithms (FTDAs) difficult to achieve high imaging performance. In this paper, a novel fast time domain algorithm (FTDA) is developed for UAVS-SAR imaging with both high efficiency and promising accuracy. By developing the hierarchical framework based on the designed spectrum alignment function, the imaging procedures can be realized recursively where back projection (BP) operations are reduced dramatically, and then, the total computational burden are decreased consequently. Moreover, the trajectory difference of the UAVS formation is particularly considered for practical applications, which will inevitably degrade the coherence among the sub-images from different UAV platforms. To address this problem, a correction procedure is designed according to the distributed geometrical configuration. As the coherence between the sub-images is adequately maintained, the data-driven motion compensation (MOCO) is readily developed to remove the residual phase errors to achieve desirable imaging performance. Simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.