There is an abundance of GNSS receivers deployed around the world providing measurements and PNT information, which are currently combined into networks for a variety of GNSS processing and monitoring applications. However, these receivers remain vulnerable and cannot be used to isolate all sources of error on all GNSS signals and constellations due to the processed nature of the receiver output, which is receiver-specific and sometimes also due to the encrypted nature of the transmitted GNSS signals. 

To overcome these difficulties, it is proposed to develop the 

A Block-box, an external RF2RF device targeting the enhancement of any COTS GNSS receiver, can help to overcome these difficulties. 

Therefore, the main objective of this activity is the design of an external RF2RF GNSS receiver enhancement device providing following capabilities: 

• Local GNSS threats and system anomalies detection and classification based on an on-bard implemented Artificial Intelligence engine
• GNSS threats mitigation, signal cleaning and retransmission at Radio Frequency to support real-time signal cleaning for a variety of applications at user level for static and dynamic GNSS asset
• Cloud data processing and sharing, enabling design and fine tuning of the AI/ML algorithms and allowing for GNSS threats and system anomalies detection and classification on a regional scale when multiple Block-box unit are deployed at different sites