DUG见解：大数据分析和可视化软件包及其使用radio telescopes的无源雷达空间态势感知演示
DUG Insight: A software package for big-data analysis and visualisation, and its demonstration for passive radar space situational awareness using radio telescopes
摘要：
随着SKA时代对支持大规模射电天文数据集处理和分析的软件需求的增加，我们展示了DUG Insight的交互式工作流程构建，数据挖掘，处理和可视化功能。我们通过处理工程开发阵列（EDA 2）在三天内产生的近68，000张全天无线电图像来测试DUG Insight的性能和灵活性。处理的目的是被动地探测和识别已知的常驻空间物体（RSO：在轨卫星和碎片），并研究如何利用无线电干涉测量法被动地监测航空器交通。这些信号是可以观测到的，这是由于地面调频无线电信号反射回地球和带外传输从RSO。这种对低地球轨道和空气空间环境的监视有助于提高对空间情况的认识和飞行器跟踪技术。根据观测结果，我们在距离EDA 2 1,500公里的范围内对19个独特的RSO进行了40次探测。这是对先前发表的同一数据集的研究的重大改进，并展示了DUG Insight的灵活功能，允许大规模处理复杂数据集。我们的DUG Insight工作流程的未来增强将旨在实现实时采集，检测未知的RSO，并继续处理来自SKA相关设施的数据。
英文摘要：
As the demand for software to support the processing and analysis of massive radio astronomy datasets increases in the era of the SKA, we demonstrate the interactive workflow building, data mining, processing, and visualisation capabilities of DUG Insight. We test the performance and flexibility of DUG Insight by processing almost 68,000 full sky radio images produced from the Engineering Development Array (EDA2) over the course of a three day period. The goal of the processing was to passively detect and identify known Resident Space Objects (RSOs: satellites and debris in orbit) and investigate how radio interferometry could be used to passively monitor aircraft traffic. These signals are observable due to both terrestrial FM radio signals reflected back to Earth and out-of-band transmission from RSOs. This surveillance of the low Earth orbit and airspace environment is useful as a contribution to space situational awareness and aircraft tracking technology. From the observations, we made 40 detections of 19 unique RSOs within a range of 1,500 km from the EDA2. This is a significant improvement on a previously published study of the same dataset and showcases the flexible features of DUG Insight that allow the processing of complex datasets at scale. Future enhancements of our DUG Insight workflow will aim to realise real-time acquisition, detect unknown RSOs, and continue to process data from SKA-relevant facilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
