An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams.

Area of Science:

• Navigation Systems
• Signal Processing
• Cybersecurity

Background:

• Global Navigation Satellite System (GNSS) security is vital.
• Traditional spoofing detection methods suffer from low detection rates and limited coverage.
• Existing techniques like correlation peak distortion analysis have limitations.

Purpose of the Study:

• To propose an improved method for GNSS spoofing detection.
• To enhance detection accuracy and coverage compared to existing techniques.
• To introduce the eye diagram detection method based on the multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP).

Main Methods:

• Utilized the multiscale Canny algorithm with minimum misjudgment probability (MSC-MMP) for edge extraction from receiver correlation value-generated eye diagrams.
• Calculated image thresholds using minimum misjudgment probability to ensure edge accuracy.
• Applied the EDDM-MSC-MMP method for detecting subtle changes indicative of spoofing signals.

Main Results:

• The MSC-MMP algorithm demonstrated superior performance over traditional edge extraction methods, achieving an optimal dataset scale F score (ODS-F) improvement of over 0.072.
• The EDDM-MSC-MMP method increased spoofing detection coverage by over 60% compared to Signal Quality Monitoring (SQM) and Carrier-to-Noise Ratio methods.
• Achieved the highest detection rate on the TEXBAT dataset.

Conclusions:

• The EDDM-MSC-MMP method offers a significant advancement in GNSS spoofing detection reliability and coverage.
• This approach provides an effective solution for identifying spoofing signals in GNSS.
• The study highlights the potential of eye diagram analysis combined with advanced algorithms for robust GNSS security.