Conclusion

EagleEye introduces the leader-follower, mixed-resolution constellation organization and operating model for computational nanosatellite constellations. Leveraging heterogeneity enables an EagleEye constellation to provide coverage similar to a wide-angle, low-resolution imaging constellation, while providing high-resolution images. The novel target clustering technique and scheduling algorithm make it possible for a leader satellite to task followers with a set of actuations, providing an increase in constellation autonomy and eliminating the need for human tasking interventions. Overall, EagleEye provides a reduction of up to 4.3× in the constellation size required to provide high-resolution images, with gains that translate across several application domains.

EagleEye 为计算型纳卫星星座引入了主从式、混合分辨率的星座架构与运行模型。通过利用异构性，EagleEye星座能够在提供高分辨率图像的同时，实现与广角、低分辨率成像星座相当的覆盖范围。新颖的目标聚类技术和调度算法使主导卫星能够为跟随卫星规划一系列驱动动作，从而提升了星座的自主性 (autonomy)，并消除了人工任务干预的需求。总体而言，EagleEye将在提供高分辨率图像方面所需的星座规模减少了高达4.3倍，其优势可推广至多个应用领域。