基于无人机集群模式的合成孔径雷达成像技术研究

无人机集群具备抗毁性高、功能分布化、并行感知、协同执行、多视角全方位观测等重要优势，是无人机技术的最新发展方向。申请人整合无人机集群与合成孔径雷达二者的技术优势，开创性地提出了一种基于无人机集群的合成孔径雷达（UAVS-SAR）成像模式，大大提高了无人机集群全天时全天候的主动探测能力。本课题针对UAVS-SAR成像中的关键技术难点展开研究：1）研究基于树状分层运算架构的时域快速成像，实现多个无人机平台的SAR信号相干处理，同时降低无人机平台之间的数据传输速率要求，提升系统的实时性能；2）简化多平台的未知误差叠加问题，设计NsRCM和相位误差联合处理的自聚焦方法，消除误差对成像的影响；3）根据相位误差在空间域的连续特性，研究估计误差预处理方法，消除图像融合引入的图像不连续。预期得到一套稳健高效的时域快速成像方法，为UAVS-SAR在工程上实现时域快速成像与实时系统开发提供理论依据和算法基础。

Unmanned aerial vehicle swarm (UAVS) has significant advantages such as high invulnerability, distributed functions, parallel perception, collaborative implementation, and multi-angle and omnidirectional observation, which has become newest trend of UAV technique. By integrating the technique advantages of both UAVS and synthetic aperture radar, a novel imaging mode of unmanned aerial vehicle swarm synthetic aperture radar (UAVS-SAR) is proposed, which significantly improves the proactive observing capability in all weather and all day-and-night. This new project will study on the technique issues of UAVS-SAR imaging: 1) a new fast algorithm based on tree layering structure will be studied for multi-UAV SAR signal coherent processing, which will reduce the requirement for data transmission rate among the UAV platforms to improve the real-time performance; 2) By simplifying the problem of unknown error blending from multi-UAV, a coherent autofocusing for both APE and NsRCM correction will be studied to improve the accuracy of error estimation and compensation, which can remove the adverse effect of error to the SAR image; 3) By exploring the continuity of phase error in spatial domain, a preprocessing technique for error estimation will be studied to remove the discontinuity of image. Through the research achievements in this project, a practical approach for UAVS-SAR processing with robustness and high-efficiency will be finally obtained, which will also provide theoretical and algorithmic foundations of fast time-domain processing for practical UAVS-SAR imaging and real-time system development.