CAREER: Secure and ubiquitous position, navigation and timing

职业：安全且无处不在的位置、导航和授时

• 批准号: 1845833
• 负责人: Pau Closas
• 金额: $ 50万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845833&HistoricalAwards=false
• 关键词: CAREER; Secure; ubiquitous; position; navigation

The long-term objective of this project is to enable secure position, navigation and timing (PNT) anywhere, a longstanding goal since ancient times. Currently, the most pervasive PNT technology is Global Navigation Satellite Systems (GNSS). However, GNSS exhibits major vulnerabilities and limitations under certain conditions such as indoor navigation, malicious attacks like jamming or spoofing, and other GNSS-denied scenarios. This five-year career-development plan is a comprehensive research, education, and outreach program that will address GNSS limitations and form the next generation of PNT engineers. The combination of both elements will unlock the potential of secure and precise PNT using GNSS technology for indoor, as well as outdoor use. The objectives of the proposed research and education plan are: 1) to forge a novel, overarching signal processing paradigm to design advanced GNSS receivers. The PI will adopt direct-positioning to yield unprecedented ultra-high-sensitivity performance, enabling GNSS indoors; 2) to further reinforce of the theory of direct-positioning by incorporating security guarantees and mechanisms by using robust statistics; and 3) to implement an ambitious educational plan that includes multifaceted activities involving the use of the open source GNSS software-defined radio (GNSS-SDR) project. In turn, the GNSS-SDR will be deeply integrated with the research endeavors as a testbed, which naturally intertwines both research and education aspects of this project following the PI's ongoing efforts. The success of the proposed research will impact many applications where PNT is provided by GNSS, for which current limitations prevent faster adoption. This includes critical infrastructures, such as the power grid, first-responder squads, unmanned and autonomous vehicles, intelligent transportation systems, precision surveying, agriculture, or mass-market applications involving hand-held devices. The project addresses a fundamental question in secure and ubiquitous PNT: what are the real limits of GNSS-denied scenarios. The research deepens around the direct-positioning paradigm, which is recognized by the GNSS community as a breakthrough in the design and understanding of GNSS technology. The main intuition is that synchronization parameters of all satellites are intimately related among them through the receiver position and velocity. The fact that all those signals are received at the same location and time instant is a strong constraint that is not exploited in current PNT schemes. In contrast, direct position estimation (DPE) jointly processes those signals, increasing its sensitivity and robustness to common propagation challenges such as weak signal, amplitude fading, multipath, jamming, spoofing, or ionospheric scintillation, therefore yielding to superior resilience in currently GNSS-denied environments. The research goals are structured in three thrusts: 1) to establish a DPE framework that allows for ultra-high-sensitivity receivers to operate in extreme environments, such as indoors. Tasks include derivation of the fundamental estimation bounds as well as investigation of the signal processing and sensor fusion methods that enable to efficiently attain those limits. The project will investigate the integration of DPE with real-time kinematics, extending the availability of high-precision PNT; 2) to secure GNSS receivers against malicious attacks, and ionospheric scintillation. DPE schemes will be leveraged to combat such interferences in combination with robust statistics, machine learning, and Bayesian inference tools. As a result, a transformative, unified framework will be conceived for sub-decimetric precision GNSS receivers that are both secure and can operate in denied scenarios but are not possible using current technology; and 3) to implement and validate the developed techniques on an end-to-end GNSS-SDR receiver, around which a research and educational plan is conceived to boost and consolidate the area of secure and ubiquitous PNT.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.

该项目的长期目标是实现任何地方的安全定位、导航和定时（PNT），这是自古以来的一个长期目标。目前，最普遍的PNT技术是全球导航卫星系统（GNSS）。然而，GNSS在某些条件下表现出重大的脆弱性和局限性，例如室内导航、恶意攻击（如干扰或欺骗）以及其他GNSS拒绝场景。这个为期五年的职业发展计划是一个全面的研究，教育和推广计划，将解决GNSS的局限性，并形成下一代PNT工程师。这两个要素的结合将释放使用GNSS技术进行室内和室外使用的安全和精确PNT的潜力。拟议的研究和教育计划的目标是：1）打造一个新颖的，总体的信号处理范式，设计先进的全球导航卫星系统接收器。PI将采用直接定位，以产生前所未有的超高灵敏度性能，使GNSS能够在室内使用; 2）通过使用可靠的统计数据，纳入安全保障和机制，进一步加强直接定位理论; 3）实施雄心勃勃的教育计划，其中包括涉及使用开源GNSS软件定义无线电（GNSS-SDR）项目的多方面活动。反过来，GNSS-SDR将作为一个试验平台与研究工作深度整合，这自然会在PI的持续努力下将该项目的研究和教育方面交织在一起。拟议研究的成功将影响GNSS提供PNT的许多应用，目前的限制阻碍了更快的采用。这包括关键基础设施，如电网、急救小组、无人驾驶和自动驾驶汽车、智能交通系统、精密测量、农业或涉及手持设备的大众市场应用。该项目解决了安全和无处不在的PNT中的一个基本问题：拒绝GNSS的场景的真实的限制是什么。该研究围绕直接定位范式深化，全球导航卫星系统界认为这是设计和理解全球导航卫星系统技术方面的一个突破。主要的直觉是，所有卫星的同步参数通过接收机的位置和速度在它们之间密切相关。所有这些信号在相同位置和时刻被接收的事实是在当前PNT方案中未被利用的强约束。相比之下，直接位置估计（DPE）联合处理这些信号，增加其对常见传播挑战（例如弱信号、幅度衰落、多径、干扰、欺骗或电离层闪烁）的灵敏度和鲁棒性，因此在当前GNSS拒绝的环境中产生上级弹性。研究目标分为三个方面：1）建立一个DPE框架，允许超高灵敏度接收器在极端环境下工作，如室内。任务包括推导的基本估计界限，以及调查的信号处理和传感器融合方法，使有效地达到这些限制。该项目将研究DPE与实时运动学的集成，扩展高精度PNT的可用性; 2）保护GNSS接收器免受恶意攻击和电离层闪烁。将利用DPE方案与强大的统计、机器学习和贝叶斯推理工具相结合来对抗这种干扰。因此，将为亚分米精度的全球导航卫星系统接收器设想一个变革性的统一框架，这些接收器既安全又可在拒绝的情况下运行，但使用当前技术是不可能的;以及3）在端到端GNSS-SDR接收机上实现和验证所开发的技术，该奖项反映了NSF的法定使命，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，认为值得支持。

项目成果

Neural Network-Based OFDM Receiver for Resource Constrained IoT Devices

适用于资源受限物联网设备的基于神经网络的 OFDM 接收器

• DOI: 10.1109/iotm.001.2200051
• 发表时间: 2022
• 期刊: IEEE Internet of Things Magazine
• 作者: Soltani, Nasim;Cheng, Hai;Belgiovine, Mauro;Li, Yanyu;Li, Haoqing;Azari, Bahar;D'Oro, Salvatore;Imbiriba, Tales;Melodia, Tommaso;Closas, Pau
• 通讯作者: Closas, Pau

Sample generation for the spin-fermion model using neural networks

使用神经网络生成自旋费米子模型的样本

• DOI: 10.1103/physrevb.106.205112
• 发表时间: 2022
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Stratis, Georgios;Weinberg, Phillip;Imbiriba, Tales;Closas, Pau;Feiguin, Adrian E.
• 通讯作者: Feiguin, Adrian E.

Data Decoding Analysis of Next Generation GNSS Signals

下一代 GNSS 信号的数据解码分析

• 发表时间: 2019
• 期刊: The International Technical Meeting of the Satellite Division of The Institute of Navigation
• 作者: Ortega, Lorenzo;Closas, Pau;Poulliat, Charly;Boucheret, Marie-Laure;Aubault-Roudier, Marion
• 通讯作者: Aubault-Roudier, Marion

On GNSS Jamming Threat from the Maritime Navigation Perspective

从海上导航角度论GNSS干扰威胁

• DOI: 10.23919/fusion43075.2019.9011348
• 发表时间: 2019
• 期刊: 2019 22th International Conference on Information Fusion (FUSION)
• 作者: D. Medina;Christoph Lass;E. P. Marcos;R. Ziebold;P. Closas;Jesús García
• 通讯作者: Jesús García

Personalized Federated Learning over non-IID Data for Indoor Localization

• DOI: 10.1109/spawc51858.2021.9593115
• 发表时间: 2021-07
• 期刊: 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
• 作者: Peng Wu;T. Imbiriba;Junha Park-;Sunwoo Kim;P. Closas