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. In this project we are interested in 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.  In this context, this project will investigate GNSS receivers that are resilient to interferences without requiring detection and classification. We will leverage robust statistics to design methods that require little modifications with respect to state-of-the-art receiver architectures, keeping SWAP requirements comparable to those from standard GNSS receivers.

This research advanced our knowledge on 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 interferences, processes which are typically the cause of error. The project designed 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 interferences. The development of such receiver enhancements will allow for large-scale deployments of GNSS receivers that are more resilient and reliable.

Particularly, the outcomes of the project contributed to make GNSS receivers robust in different critical aspects involved in the operation of the receiver. Namely: 1) We developed an overarching methodology to mitigate the effect of interference and jamming signals at the baseband processing level of GNSS receiver. That is in acquisition and tracking stages of the receiver, which are typically vulnerable to jamming attacks. We leveraged robust statistics to redesign those stages of the receiver, substituting typical mean square error (MSE) minimization with more resilient choices for the cost function to optimize; 2) We characterized the effects of jamming signals on the decoding of the navigation message for existing signals and future recommendations. Additionally, we investigated and proposed novel countermeasures. Those are based on making the estimated log-likelihood ratio quantities more robust to those interferences. For that purpose we considered a Bayesian approach, where the uncertainty due to interferences is accounted for in developing robust log-likelihood ratio (LLR) values; and 3) We investigated how to make the last stage of the receiver (i.e., where the position solution is computed given pseudoranges). This stage is typically implemented through a least squares (for single point positioning, SPP) or using a Kalman filter (for precise point positioning, PPP; or real-time kinematics, RTK). We investigated robust SPP and robust RTK approaches leveraging robust statistics and Variational Bayes approaches for SPP and RTK, respectively. The results show that the anti-jamming techniques can virtually eliminate a variety of interferences and outperform current solutions. The feasibility of the various approaches was demonstrated with both synthetic and real-data recordings

The outcomes of the project generated a total of 18 journal articles and 20 conference papers.

Last Modified: 05/28/2021
Modified by: Pau Closas