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Securing Global Navigation Satellite System Infrastructures

September 4, 2018

ECE Assistant Professor Pau Closas was awarded a $160K NSF grant for "securing GNSS-based infrastructures."

Pau Closas, assistant professor, electrical and computer engineering, was awarded a $160K National Science Foundation grant for securing GNSS-based infrastructures. GNSS stands for Global Navigation Satellite Systems, encompassing all those positioning systems (like GPS or Galileo) that use a constellation of satellites to offer positioning and timing services.

“This project, in particular, is about building more resilient GNSS receivers against certain threats,” he said. “It is basically about making GPS receivers more robust to intentional and unintentional interferences.”

Closas has been working with GPS receivers for many years, and for all those years he could build a network of collaborators from all over the world. He moved to Boston from Barcelona in December 2016 and started his career at Northeastern. Closas and his team had been researching and publishing preliminary results in this project area for years, prior to the recent NSF award.

The main idea of the project is developing effective and affordable techniques for GNSS receivers. It aims to secure GNSS receivers from jamming interferences, which can be caused either by malicious users trying to deny positioning service or by adjacent communication signals that are perceived as interferences to the GNSS receiver. This is particularly alarming considering that many critical infrastructures of a country — like the power grid, airports, wireless communication networks, transportation systems, or electronic trading — are relying on GNSS services, for positioning, and also for synchronizing accurately different stations. Therefore, a potential disruption of GNSS would heavily impact a countries’ principal activities, for which this project provides solutions.

“The main problem is that you can actually go to Ebay and buy a jammer that denies GPS on 1 km for $10,” Closas mentioned.

Existing solutions are either not effective or very expensive. While they require the detection and classification of the interference and constitute a single point of error in the process, Closas’ project is totally different. It leverages robust statistics to build a theoretically sound framework to build robust GNSS receivers that do not require knowledge about the inference signals, which are treated as outliers to the signal.

Other than jamming, there are other major GNSS interferences such as spoofing. This consists of an attack where malicious users send “fake” signals that look like real signals from satellites, with the intention of deceiving the receiver by superseding legitimate signals and modify them such that the GNSS receiver calculates a wrong location or timing. Closas is working on two projects simultaneously involving this and other types of GNSS interferences. Those developments will allow a large-scale implementation of more robust GNSS receivers.

“In the context of GNSS receiver design there are a lot of interesting challenges to be addressed by the signal processing community,” he said.



Abstract Source: NSF

This project develops novel anti-jamming techniques for Global Navigation Satellite Systems (GNSS) that are effective, yet computationally affordable. GNSS is ubiquitous in civilian, security and defense applications, causing a growing dependence on such technology for position and timing purposes, particularly in critical infrastructures. The threat of a potential disruption of GNSS is real and can lead to catastrophic consequences. This project studies methods to secure GNSS receivers from jamming interference, and doing so within size, weight, and power (SWAP) requirements. Existing solutions are either bulky and not cost-effective, such as those based on antenna array technology, or specifically adapted to an interference type. In addition, most of these solutions require the detection and classification of the interference before mitigating its effects, which constitutes a single point of error in the process. This project will investigate GNSS receivers that are resilient to interference without requiring detection and classification, by leveraging robust statistics to design methods that require few modifications with respect to state-of-the-art receiver architectures, keeping SWAP requirements comparable to those from standard GNSS receivers. The findings will be implemented and validated on an end-to-end GNSS software-defined radio receiver, successfully transitioning research into practice. Educational activities are closely integrated with this research agenda, including a course developed by the principal investigator and outreach activities.

This research advances knowledge of how robust statistics can be leveraged to design cost-effective and efficient mitigation techniques for anti-jamming GNSS. The main premise of the project is that most interference sources have a sparse representation, on which they can be seen as outliers to the nominal signal model. Tools from robust statistics are then used to discard those outliers in a sound manner, identifying and substituting specific critical operations in GNSS processing. This approach avoids the need for detecting and estimating interference, processes which can cause errors. The project envisions a lightweight, yet robust, GNSS receiver that can be easily adopted in substitution of current GNSS receivers that are supporting operation of critical infrastructures. It will enable reliable and precise anti-jamming technology with drastic SWAP and cost improvements. Particularly, the project will provide a GNSS receiver solution that can cope with common jamming interference. The development of such receiver enhancements, along with their validation in a software receiver, will allow for large-scale deployments of GNSS receivers that are more resilient and reliable.

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.