论文标题
无人机合成孔径雷达的轨迹和资源优化
Trajectory and Resource Optimization for UAV Synthetic Aperture Radar
论文作者
论文摘要
在本文中，我们研究了配备了合成孔径雷达（SAR）系统的轻质旋转无人驾驶汽车（UAV）的轨迹和资源优化。无人机的使命是对特定的感兴趣领域（AOI）进行SAR成像。在此设置中，需要与基站（BS）的实时沟通以促进无人机实时任务计划。为此，制定了非凸混合企业非线性程序（MINLP），以使无人机资源和三维（3D）轨迹共同优化，以最大化无人机雷达地面覆盖范围。我们提出了基于连续的凸近似（SCA）来解决问题的低复杂性子优越算法，并执行有限的搜索以优化无人机横穿的总距离以获得最大覆盖率。我们表明，与采用恒定功率进行通信或雷达成像的基准方案相比，所提出的3D轨迹计划至少提高了雷达地面覆盖率的70％。我们还表明，将BS定位在AOI附近可以显着改善无人机的雷达覆盖率。
In this paper, we study the trajectory and resource optimization for lightweight rotary-wing unmanned aerial vehicles (UAVs) equipped with a synthetic aperture radar (SAR) system. The UAV's mission is to perform SAR imaging of a given area of interest (AoI). In this setup, real-time communication with a base station (BS) is required to facilitate live mission planning for the drone. For this purpose, a non-convex mixed-integer non-linear program (MINLP) is formulated such that the UAV resources and three-dimensional (3D) trajectory are jointly optimized for maximization of the drone radar ground coverage. We present a low-complexity sub-optimal algorithm based on successive convex approximation (SCA) for solving the problem, and perform a finite search to optimize the total distance traversed by the UAV for maximal coverage. We show that the proposed 3D trajectory planning achieves at least 70% improvement in radar ground coverage compared to benchmark schemes employing constant powers for communication or radar imaging. We also show that positioning the BS near the AoI can significantly improve the radar coverage of the UAV.
