New Concept for Evolutive Mitigation of RFI to GNSS
Radio frequency interference, intentional or not, is considered to be among the main threats to safety-critical and commercial GNSS applications. This includes out-ofband emissions, privacy-protection devices (PPD) also known as in-car jammers, etc. Consequently, many COTS GNSS receivers nowadays integrate mitigation strategies for RF interferences (jamming and spoofing). Moreover, one can note that PPDs are continuously evolving, becoming more complex and consequently introducing new challenges to RFI mitigation. Usually RFI mitigation is a built-in feature, and even if based on some adaptive signal processing algorithms, those features are static and do not evolve over time. Therefore, they cannot not really adapt to the evolution of the RFI landscape, which may include new out-of-band emissions or new interferences with different signatures.
In the field of IT security, antiviruses and anti-malware protection are regularly updated to cope with new threats, as those are continuously monitored and used to design those updates. A similar approach could be applied to GNSS, where new RFI threats could be discovered and managed over time. By having a flexible and reconfigurable DSP frontend monitoring the GNSS bands, before the usual baseband stages of a GNSS receiver, it could detect the presence of interference and apply the relevant mitigation. The processing could be implemented in software modules with FPGA (Field Programmable Gate Array) support for real-time operation with reduced energy consumption. These modules would be continuously improved by service.
centers/operators monitoring the RFI signatures, which updates the user equipment as an antivirus would do.
The objective of the proposed activity is to develop a new concept of RFI mitigation technology for GNSS, taking inspiration from IT antivirus and antimalware protection strategies, and leveraging on low cost Software-Defined Radios (SDR). The mitigation strategies will also leverage on Machine Learning and Artificial Intelligence, in combination with established RFI mitigation building blocks (notch filter, blanker, etc.).
The tasks to be performed will include:
- identification and consolidation of use cases for mass-market and commercial applications;
- identification and preliminary design of candidate architectures; - design, implementation and validation of the UE, and implementation of the training process for AI;
- testing in controlled environment, as laboratory.
The main results of the activity will provide:
- breadboard of the new RFI mitigation strategy, benchmarked with off-theshelves solutions;
- recommendations for the way forward, relating to both the technological aspects of the proposed activity (increased robustness for GNSS UE) and relating to a potential business model in GNSS, which could pave the way towards prototyping and products, for instance in the frame of NAVISP Element 2.
Results from related GSA H2020/FE projects will be duly considered and assessed.
Coordination with the Interference Monitoring Task Force (EGITF) will be ensured through EC and GSA.
Emits link: http://emits.sso.esa.int/emits/owa/emits.main