NSF-AoF: CIF: Small: Distributed AI for enhanced security in satellite-aided wireless navigation (RESILIENT)

NSF-AoF：CIF：小型：分布式 AI，用于增强卫星辅助无线导航的安全性（弹性）

• 批准号: 2326559
• 负责人: Pau Closas
• 金额: $ 60万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326559&HistoricalAwards=false
• 关键词: NSF; AoF; CIF; Small; Distributed

The goal of this project is to develop tools for interference management in geolocation applications. The use of Global Navigation Satellite Systems (GNSS) is ubiquitous in civilian, security, and defense applications. As a consequence, the threat of a potential disruption, or even malicious superseding, of GNSS is real and can lead to catastrophic consequences. Therefore, there is a growing need for protecting GNSS against intentional and unintentional interference sources. Particularly, this project investigates a distributed framework to detect and classify threats using hybrid physics- and data-driven models, information which is then used to globally localize the sources of interference. This project is composed of a team of researchers from US and Finland, in a joint effort to advance worldwide security of GNSS against existing threats while providing an excellent opportunity for students to participate in an international project on cutting-edge technologies and methodology development. The team has planned workshops and activities in order to foster a fruitful collaboration between the international research team and students.This project considers problems related to distributed, collaborative learning tasks, where data-driven AI-models are leveraged to augment physics-based solutions for improved capabilities. The specific goals of the project are divided into three research goals. The first goal investigates the use of deep learning for detection/classification of interference and fusion of multiple correlated classifiers providing local threat detection probabilities. The second goal aims to localize and track the interference sources through the creation of global threat probability maps. This goal is achieved by advancing the field federated learning in a threefold way: (i) to efficiently digesting non-independent and identically distributed data; (ii) combining with active learning methods, whereby moving agents sample specific locations to improve estimation performance; and (iii) investigating federated meta-learning strategies that use task-level knowledge to improve global learning. The third goal of the project investigates the use of those global threat maps to mitigate their effects on collaborative receivers using robust factor graph optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目的是开发用于地理位置应用中干扰管理的工具。全球导航卫星系统（GNSS）的使用在平民，安全和国防应用中无处不在。结果，GNSS的潜在破坏甚至恶意取代的威胁是真实的，可能导致灾难性后果。因此，越来越需要保护GNS免受故意和无意的干扰来源。特别是，该项目研究了一个分布式框架，以使用混合物理和数据驱动的模型来检测和分类威胁，然后将其用于全球范围内定位干扰源。该项目由来自美国和芬兰的研究人员组成，共同努力促进全球GNS的安全性，以防止现有威胁，同时为学生提供了一个极好的机会，让学生参加了一项有关尖端技术和方法发展的国际项目。该团队计划了研讨会和活动，以促进国际研究团队与学生之间的富有成果的合作。该项目考虑了与分布式协作，协作学习任务相关的问题，在该问题中，将数据驱动的AI模型利用以增强基于物理的解决方案，以提高功能。该项目的具体目标分为三个研究目标。第一个目标调查了使用深度学习来检测/分类的干扰和融合多个相关分类器，这些分类器提供了局部威胁检测概率。第二个目标旨在通过创建全球威胁概率图来定位和跟踪干扰源。通过以三倍的方式推进联合学习的领域来实现此目标：（i）有效消化非独立和相同分布的数据； （ii）与主动学习方法结合使用，从而使转移代理采样特定位置以提高估计性的性能； （iii）调查使用任务级知识来改善全球学习的联合元学习策略。该项目的第三个目标调查了使用这些全球威胁图来减轻其对协作接收器的影响，使用强大的因素图优化。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来获得支持的。