GNSS Spoofing Detection Using Predicted Orbital Data
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Solution Overview
Problem
GNSS receivers are susceptible to spoofing threats, leading to erroneous position outputs that can cause safety hazards in aircraft navigation and landing, affecting critical systems and deteriorating required navigation performance.
Innovation Solution
A method and system for detecting GNSS spoofing by monitoring satellite signals, calculating predicted orbital information, and comparing it with current data to identify discrepancies exceeding threshold levels, generating a spoofing alert signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS receivers use standard 24 channel design, then device complexity is reduced, but reliability deteriorates due to susceptibility to spoofing threats
Solution Approach 1:
The system performs preliminary actions by calculating predicted orbital information values before comparing them with current received values. This predictive approach allows the receiver to establish expected satellite parameters in advance, enabling detection of spoofing attempts before they can compromise navigation accuracy. The predicted values serve as a reference framework that proactively identifies anomalies.
Solution Approach 2:
The system implements feedback by continuously comparing current orbital information values against predicted values and generating discriminator values. When discriminator values exceed threshold levels, the system provides feedback through spoofing alert signals that trigger corrective actions. This closed-loop feedback mechanism enables real-time detection and response to spoofing threats without requiring complex hardware modifications.
2Measurement precision
If GNSS receivers process satellite signals without verification, then ease of operation is maintained, but measurement precision deteriorates due to erroneous position outputs
Solution Approach 1:
The GNSS receiver performs self-service by autonomously verifying its own received satellite signals against predicted orbital information. The system independently calculates expected satellite positions, Doppler shifts, and other orbital parameters, then compares these with actual received values. This self-verification capability enables the receiver to detect spoofing attempts and maintain position accuracy without requiring external verification systems or complex additional hardware.
Solution Approach 2:
The system replaces mechanical or hardware-based verification methods with computational substitution. Instead of using complex antenna arrays or multiple physical receivers for verification, the system uses software-based prediction algorithms that calculate expected orbital information and compare it with received signals. This computational approach achieves high measurement precision while avoiding the complexity of additional mechanical verification systems.
3Reliability
If spoofing detection algorithms are implemented, then reliability improves, but loss of time increases due to calculation and comparison operations
Solution Approach 1:
The system performs preliminary calculations of predicted orbital information values in advance, before actual satellite signal comparison is needed. By pre-computing expected satellite positions, velocities, and orbital parameters, the system reduces the real-time processing burden during critical navigation operations. This preliminary action allows rapid comparison with current received values, minimizing detection processing time while maintaining high navigation safety.
Solution Approach 2:
The system applies partial verification by focusing computational resources on the most critical orbital parameters for spoofing detection, such as satellite position and Doppler shift. Rather than verifying every aspect of satellite signals in full detail, the system performs targeted comparisons on key discriminators that are most sensitive to spoofing attempts. This selective partial verification achieves reliable spoofing detection while reducing overall processing time and computational load.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances navigation safety by detecting and mitigating spoofing threats, ensuring accurate position computation and enabling corrective actions to avoid hazardous conditions.
Implementation Method 1
obtaining a current time and one or more current orbital information values from the at least one satellite receiver, wherein the current one or more orbital information values comprise satellite orbital position, Doppler shift, or dilution of precision
Data Source
AI summary
A method for detecting spoofing comprises monitoring GNSS satellite signals with a GNSS receiver; obtaining current time, current orbital information values, Doppler shift, and/or dilution of precision from the GNSS receiver, with the current orbital information values comprising orbital position with respect to a current position of the GNSS receiver; retrieving past orbital information values comprising orbital position, and calculating predicted orbital information values based on the past orbital information values, with respect to the current time from the receiver, with the predicted orbital information values comprising orbital position with respect to the current position of the receiver. The method compares the predicted orbital information values, Doppler shift, and/or dilution of precision, with the current orbital information values to obtain discriminator values; determines whether the discriminator values are greater than threshold levels; and outputs a spoofing alert signal when the discriminator values are greater than the threshold levels.