A DATA-INTENSIVE APPROACH TO EXPLOIT NEW GNSS SCIENCE OPPORTUNITIES

V. Navarro, J. Ventura-Traveset
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Abstract

With the current GNSS infrastructure development plans, over 120 GNSS satellites (including European Galileo satellites)will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis.This global and permanent GNSS infrastructure constitutes a major opportunity for GNSS science applications. In themeantime, recent advances in technology have contributed "de-facto" to the deployment of a large GNSS receiver arraybased on Internet of Things (IoT), affordable smart devices easy to find in everybody’s pockets. These devices – evolvingfast at each new generation – feature an increasing number of capabilities and sensors able to collect a variety ofmeasurements, improving GNSS performance. Among these capabilities, Galileo dual band smartphones receivers andAndroid’s support for raw GNSS data recording represent major steps forward for Positioning, Navigation and Timing (PNT)data processing improvements. Information gathering from these devices, commonly referred as crowdsourcing, opensthe door to new data-intensive analysis techniques in many science domains. At this point, collaboration between variousresearch groups is essential to harness the potential hidden behind the large volumes of data generated by thiscyberinfrastructure. Cloud Computing technologies extend traditional computational boundaries, enabling execution ofprocessing components close to the data. This paradigm shift offers seamless execution of interactive algorithms andanalytics, skipping lengthy downloads and setups. The resulting scenario, defined by a GNSS Big Data repository with colocatedprocessing capabilities, sets an excellent basis for the application of Artificial Intelligence / Machine Learning (ML)technologies in the context of GNSS. This unique opportunity for science has been recognized by the European SpaceAgency (ESA) with the creation of the Navigation Scientific Office, which leverages on GNSS infrastructure to deliverinnovative solutions across multiple scientific domains.
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利用新的GNSS科学机会的数据密集型方法
根据目前的全球导航卫星系统基础设施发展计划,超过120颗全球导航卫星系统卫星(包括欧洲伽利略卫星)将在本十年内在几个频率上不间断地永久提供连续数据。这种全球性和永久性的全球导航卫星系统基础设施为全球导航卫星系统科学应用提供了重大机遇。与此同时,最近的技术进步“事实上”促进了基于物联网(IoT)的大型GNSS接收器阵列的部署,这是一种价格合理的智能设备,很容易在每个人的口袋里找到。这些设备每一代都在快速发展,具有越来越多的功能和传感器,能够收集各种测量数据,提高GNSS性能。在这些功能中,伽利略双频智能手机接收器和android对原始GNSS数据记录的支持代表了定位、导航和授时(PNT)数据处理改进的主要步骤。从这些设备中收集信息,通常被称为众包,在许多科学领域为新的数据密集型分析技术打开了大门。在这一点上,各个研究小组之间的合作对于利用这个网络基础设施产生的大量数据背后隐藏的潜力至关重要。云计算技术扩展了传统的计算边界,使处理组件能够在接近数据的地方执行。这种模式的转变提供了交互式算法和分析的无缝执行,跳过了冗长的下载和设置。由此产生的场景由具有协同处理能力的GNSS大数据存储库定义,为人工智能/机器学习(ML)技术在GNSS环境中的应用奠定了良好的基础。欧洲航天局(ESA)认识到这一独特的科学机会,并创建了导航科学办公室,该办公室利用GNSS基础设施提供跨多个科学领域的创新解决方案。
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