GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation.

Area of Science:

• Navigation Systems
• Signal Processing
• Cybersecurity

Background:

• Global Navigation Satellite System (GNSS) is vulnerable to spoofing attacks, threatening reliable positioning.
• Maximum Likelihood Estimation (MLE) is a positioning technique effective for multipath rejection and weak signal processing.
• Spoofing attacks distort the MLE cost function, indicating potential for anti-spoofing applications.

Purpose of the Study:

• To develop an estimation-cancellation approach for detecting GNSS spoofing attacks.
• To recover accurate navigation solutions in the presence of spoofing.
• To evaluate the anti-spoofing performance of the proposed method.

Main Methods:

• Utilizing Maximum Likelihood Estimation (MLE) for positioning and spoofing detection.
• Deriving a statistic based on the Generalized Likelihood Ratio Test (GLRT) for spoofing detection.
• Decomposing the MLE cost function to validate signal consistency.
• Conducting simulations and experiments with real-world spoofing data.

Main Results:

• The proposed method effectively detects spoofing attacks and recovers navigation solutions.
• Performance is validated through theoretical analysis, simulations, and real-world experiments.
• The approach successfully identifies spoofing even with minimal code phase differences (less than one code chip).

Conclusions:

• The MLE-based estimation-cancellation approach offers a robust solution for GNSS anti-spoofing.
• This method significantly improves the availability and reliability of GNSS services under spoofing threats.
• The technique demonstrates practical applicability in real-world GNSS spoofing scenarios.