论文标题
在高性能计算环境中处理众包飞机的观察
Processing of Crowdsourced Observations of Aircraft in a High Performance Computing Environment
论文作者
论文摘要
随着无人飞机系统（UASS）继续集成到美国国家空域系统（NAS）中，有必要量化无人驾驶飞机和载人飞机之间空中碰撞的风险，以支持法规和标准发展。监管机构和标准发展组织都使用飞机飞行的概率模型广泛使用了Monte Carlo Collision风险分析模拟。我们先前已经确定，基于地面传感器的社区网络Opensky Network对载人飞机的观察适合开发低海拔环境的模型。这项工作概述了在林肯实验室超级计算中心设计和部署的高性能计算工作流程，以处理飞机的39亿观察。然后，我们使用超过250,000的飞行小时在地面高度或以上培训了25万多个飞行小时。工作流程的一个关键功能是，所有飞机观测值和支持数据集都可以作为开源技术可用或已发布给公共领域。
As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Both regulators and standards developing organizations have made extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. We've previously determined that the observations of manned aircraft by the OpenSky Network, a community network of ground-based sensors, are appropriate to develop models of the low altitude environment. This works overviews the high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process 3.9 billion observations of aircraft. We then trained the aircraft models using more than 250,000 flight hours at 5,000 feet above ground level or below. A key feature of the workflow is that all the aircraft observations and supporting datasets are available as open source technologies or been released to the public domain.
