With more GNSS receivers being used in critical infrastructures, GNSS vulnerabilities are becoming a major concern. Artificial intelligence (AI) and machine learning (ML) have enormous potential to revolutionize network performance monitoring and diagnostics. Network synchronization assurance and GNSS assurance in particular are areas where this technology could have a major impact. In his WSTS session, Nir Laufer reviewed challenges and limitations of the existing distributed approach for GNSS monitoring and assurance as well as the advantages of the centralized approach. He discussed whether ML-based systems using AI could provide the answer to mitigating GNSS vulnerabilities such as jamming and spoofing.

1. March, WSTS 2019
Mitigating GNSS jamming and spoofing utilizing artificial intelligence and machine learning
Centralized GNSS monitoring and
assurance
Nir Laufer, senior director, product line management, Oscilloquartz

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GNSS vulnerability – known issues

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Identifying the scale of impact can help us classify the cause
Am I the only one being affected?
Cause Impact
Segment error Global/regional
Adjacent band
transmissions
Local/regional
Spoofing Local/regional
Jamming Local/regional
Environmental Local/regional
Equipment
failure
Local

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• Each GNSS receiver is working independently
• Detection of GNSS outages are done locally
• Disadvantages and weaknesses:
• Limited/no data from external sources
• Decision can’t be made based on the “bigger picture”
• Limited memory and compute power
Limited and sub-optimal detection and decision making
Existing, distributed local (limited) approach

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The centralized-global approach
• Relevant data is collected from all the GNSS receivers in the network
• External data is collected from independent sources
• Advantages and strengths:
• Multiple sources of relevant data are available for analysis
• Decision is based on the “bigger picture”
• Unlimited memory and compute power
• Utilize AI and ML
• Can be done remotely – no dedicated HW is needed on site
Optimal detection and decision making
Can we do better?

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Centralized GNSS monitoring and assurance
GNSS
receiver
GNSS
receiver
GNSS
receiver
GNSS
receiver
GNSS monitoring and assurance
NASA servers
Weather

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Centralized GNSS monitoring and assurance
GNSS
receiver
GNSS
receiver
GNSS
receiver
GNSS
receiver
GNSS monitoring and assurance
NASA servers
Weather
Use ML and AI
Instructions are send back to
the relevant devices (e.g. go to
holdover or switch to backup)

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The following data is available from most of the commercial GNSS receivers via API
and can be collected remotely over secured interfaces (e.g. CLI-SSH/SNMPv3)
• Location:
• Latitude, longitude, altitude
• Satellites-related data
• SV, carrier to noise, health, azimuth, elevation
What data can we get for a GNSS receivers?

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• GNSS-related data:
• Satellite ephemerides
• GNSS Broadcast ephemeris files
• GNSS maintenance-related info:
• Satellite decommission
• Satellite health
• New satellite
• Special events (e.g. leap second and GPS rollover )
• Weather and space weather data:
• Atmospheric parameters
• Tropospheric path delay
• Ionospheric conditions
Better decision making based on comprehensive, meaningful data
What external data is available online?

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GNSS health – live network summary status
Help identify
regional and
global outages
(such as regional
jamming or
segment error)

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GNSS health – network historical status Help identify
periodic
patterns which
affect entire
network or sub-
network

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C/N spectrograms (heatmap)

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Things might not stay as they used to be …
How can we
automatically
detect changes
in site
conditions?

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Blind spot example – customer site
Poor signal from the
tower direction
High tower

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Example – poor installation
GNSS antenna installed
near a wall

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Useful data displays
Geo-map of the network Historical display of number of satellites in view / in use

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Useful data displays
C/N heatmap (indication of directional GNSS signal strength)Satellites usage heatmap

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Site analysis – “bad” site Help identify local reception
issue

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Site analysis – “good” site

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Historical view – comparing C/NO heatmap on different
time or different sites at same time

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GNSS antenna – signal reception
Artificial intelligence and applied machine learning | GNSS use case
ML can assist here …

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• ML and AI can help us automatically detect changes in patterns
• Safeguard GNSS signal reception in timing and synchronization networks
• Predictive maintenance through long-term satellite telemetry trending and analysis
• Detect GNSS anomalies such as signal obstruction caused by trees, walls and signal
interference (jamming and spoofing), etc.
• Allow early detection of protentional problems
• Can be scaled to very large networks
• Reduces OPEX
AI and ML allow automatic detection of a fault
The next level – AI and ML

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GNSS monitoring and assurance using AI and ML
NASA servers
Weather
AI server
Automatic detection of abnormal
patterns (patent pending)
AlertReact

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• Our solution is based on centralized GNSS monitoring and assurance
• The solution can be applicable to any GNSS receivers which are capable of
providing the minimal related data
• No need for additional hardware or to physically attend the site
• Provide network view of GNSS receivers health
• Allow remote in-depth analysis
• Utilize ML and AI for optimal and automated detection of potential GNSS service
degradation
Summary

25. Thank you
IMPORTANT NOTICE
The content of this presentation is strictly confidential. ADVA Optical Networking is the exclusive owner or licensee of the content, material, and information in this presentation.
Any reproduction, publication or reprint, in whole or in part, is strictly prohibited.
The information in this presentation may not be accurate, complete or up to date, and is provided without warranties or representations of any kind, either express or implied. ADVA
Optical Networking shall not be responsible for and disclaims any liability for any loss or damages, including without limitation, direct, indirect, incidental, consequential and special
damages, alleged to have been caused by or in connection with using and/or relying on the information contained in this presentation.
Copyright © for the entire content of this presentation: ADVA Optical Networking.
nlaufer@advaoptical.com

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GNSS vulnerability – public reports
• UK government: https://www.gov.uk/government/publications/satellite-derived-time-and-position-
blackett-review
• USA ATIS GPS vulnerability report: http://www.atis.org/01_resources/whitepapers.asp