标题： 多无人机合成孔径雷达干涉测量的数据卸载感知精度优化 显示英文标题

标题： Sensing Accuracy Optimization for Multi-UAV SAR Interferometry with Data Offloading

摘要： 无人机（UAV）与雷达成像传感器的集成，通过实现高分辨率和成本效益的遥感，彻底改变了对动态和局部地球表面过程的监测。 本文研究了部署用于多基线干涉合成孔径雷达（InSAR）感知的无人机群的感知精度优化。 在传统的单基线InSAR系统中，仅有一个合成孔径雷达（SAR）天线对从两个不同角度获取两幅SAR图像，以生成目标区域的数字高程模型（DEM）。 然而，多基线InSAR通过从不同角度聚合多个采集数据扩展了这一概念，从而显著提高了DEM的垂直精度。 此过程所需的大量计算在地面完成，因此雷达数据通过频分多址（FDMA）空地回传链路实时传输到地面站（GS）。 本工作旨在通过最小化平均DEM的高度误差来提高感知精度，同时确保感知和通信的服务质量（QoS）。 为此，使用进化算法（EAs）联合优化无人机编队、速度和通信功率分配。 我们的方法与已建立的优化方法进行了比较，包括遗传算法（GAs）、模拟退火（SA）和深度强化学习（DRL）技术。 数值结果表明，所提出的解决方案优于这些基准方案，并在多个场景中实现了分米级以下的垂直精度。 这些发现强调了协调无人机群通过雷达干涉测量提供高精度和实时地球观测的潜力。 显示英文摘要

摘要： The integration of unmanned aerial vehicles (UAVs) with radar imaging sensors has revolutionized the monitoring of dynamic and local Earth surface processes by enabling high-resolution and cost-effective remote sensing. This paper investigates the optimization of the sensing accuracy of a UAV swarm deployed to perform multi-baseline interferometric synthetic aperture radar (InSAR) sensing. In conventional single-baseline InSAR systems, only one synthetic aperture radar (SAR) antenna pair acquires two SAR images from two distinct angles to generate a digital elevation model (DEM) of the target area. However, multi-baseline InSAR extends this concept by aggregating multiple acquisitions from different angles, thus, significantly enhancing the vertical accuracy of the DEM. The heavy computations required for this process are performed on the ground and, therefore, the radar data is transmitted in real time to a ground station (GS) via a frequency-division multiple access (FDMA) air-to-ground backhaul link. This work focuses on improving the sensing precision by minimizing the height error of the averaged DEM while simultaneously ensuring sensing and communication quality-of-service (QoS). To this end, the UAV formation, velocity, and communication power allocation are jointly optimized using evolutionary algorithms (EAs). Our approach is benchmarked against established optimization methods, including genetic algorithms (GAs), simulated annealing (SA), and deep reinforcement learning (DRL) techniques. Numerical results show that the proposed solution outperforms these baseline schemes and achieves sub-decimeter vertical accuracy in several scenarios. These findings underline the potential of coordinated UAV swarms for delivering high-precision and real-time Earth observations through radar interferometry.