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

该项目的目标是开发地理定位应用中的干扰管理工具。全球导航卫星系统（GNSS）在民用、安全和国防应用中无处不在。因此，全球导航卫星系统可能中断甚至被恶意取代的威胁是真实的，并可能导致灾难性后果。因此，越来越需要保护GNSS免受有意和无意干扰源的影响。特别是，该项目研究了一个分布式框架，使用混合物理和数据驱动的模型来检测和分类威胁，然后将这些信息用于全球定位干扰源。该项目由来自美国和芬兰的一组研究人员组成，共同努力推进全球GNSS的安全性，以应对现有威胁，同时为学生提供参与尖端技术和方法开发国际项目的绝佳机会。该团队计划了研讨会和活动，以促进国际研究团队和学生之间富有成效的合作。该项目考虑了与分布式协作学习任务相关的问题，其中利用数据驱动的AI模型来增强基于物理的解决方案，以提高能力。该项目的具体目标分为三个研究目标。第一个目标研究使用深度学习来检测/分类干扰，并融合多个相关分类器，提供本地威胁检测概率。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。这一目标是通过以三种方式推进领域联邦学习来实现的：（i）有效地消化非独立和相同分布的数据;（ii）与主动学习方法相结合，从而移动代理对特定位置进行采样以提高估计性能;以及（iii）研究使用任务级知识的联邦元学习策略以提高全局学习。该项目的第三个目标是研究如何使用这些全球威胁图，通过强大的因子图优化来减轻其对协作接收者的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.