Project Description

There exists several services providing GNSS interference coverage such as gpsjam.org.

This service however is only update daily and only based on ADS-B data. We would instad like to have a real-time service based several data soruces such as:

• RTCM/RINEX data from Mapping Authorities (Kartverket),
• GNSS user data from the ground and sea,
• satelitte data,
• ADS-B data and
• more

to make a near real-time service providing coverage data of standard GNSS service and which areas that are affected by GNSS radio frequency interference (RFI) such as jamming and spoofing

Tasks and Expected Outcomes
The objective of the project is to investigate possible to make a real-time service for GNSS interference inspied by yr.no using appropriate AI methods to classify GNSS interference and provice useful data to user. This includes GNSS interference level and at what altitude GNSS interference should be expected

• A short literature review on GNSS signals and GNSS RFI
• Studing differenct data sources which can provide information about GNSS RFI
• Signal prosessing and AI
• Mapping service of GNSS RFI including taking into account topography. 

Your work will be affiliated with other students working on GNSS-R and GNSS-rFI related projects. 

Impact
Space technology plays a crucial role in achieving various Sustainable Development Goals (SDGs) set by the UN. GNSS-R has the capability to be an all-weather, near real-time detection space-based surveillance system independent clouds and systems based on trust and self-reporting such as AIS. GNSS-R has the potential to allows us to detect and monitor water vessels at sea and localize GNSS inference sources originating from sea or land. This project target

Space technology plays a crucial role in achieving various Sustainable Development Goals (SDGs) set by the UN. GNSS-R has the capability to be an all-weather, near real-time detection space-based surveillance system independent clouds and systems based on trust and self-reporting such as AIS. In Norway, space has a crucial role to play in our collective security and monitoring of critical infrastructure and the Arctic region. GNSS-R has the potential to allows us to detect and monitor water vessels at sea and localize GNSS inference sources originating from sea or land. This project target

• SDG9 Industry, innovation, and infrastructure. The outcomes have an innovative and commercial potential for industry and can contribute both to new space-based infrastructure and protection of existing critical infrastructure beyond GNSS.

• SDG16 Peace, justice and strong institutions. Maritime surveillance and GNSS interference monitoring are both relevant for this.

Who We Are Looking For
We are seeking a highly motivated final year student in Cybernetics, Electronics, or a related field with an interest either one or several of the topics

• positioning, navigation and timing (PNT) systems 
• signal processing
• AI
• Classification
• Multi-modal data processing 

Experience from subjects AI and classification sources. It is a benificial with background on GNSS such as TTT4150 Navigation systems, but not required. The project will be adapted to the student's background and goals. 

How we work
The student will be part of the NTNU SmallSat lab, a lab which typically hosts 10-20 master's student per semester. At the NTNU SmallSat Lab we encourage collaboration and try to get our group to help each other. To facilitate this, we as well as arrange common lunches and workshops where the students and supervisors can learn from each other. I some project we also implement a development process.

Supervisor(s)
For further questions please contact:

• Torleiv H. Bryne(main supervisor, NTNU/ITK), Roger Birkeland (co-supervisor, NTNU/IES), Tor Arne Johansen, (co-supervisor, NTNU/ITK)