GNSS Peak Suppression Monitoring for Spoofing Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
GNSS systems are vulnerable to spoofing attacks, which cause receivers to obtain incorrect position and timing solutions due to the interference of counterfeit signals.
Innovation Solution
A peak suppression monitor is employed to detect and mitigate spoofing by identifying distinct peaks in real-time correlation data, aligning authentic peaks with predicted data, and providing tracking commands to modify tracking points, using historical and real-time correlation data to suppress counterfeit signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional GNSS signal processing is used, then the system operates with standard complexity, but the system becomes vulnerable to spoofing attacks and cannot reliably distinguish authentic signals from counterfeit ones
Solution Approach 1:
The patent segments the correlation data into multiple analysis components: raw correlation data, predicted correlation data, and residual correlation data. By dividing the signal processing into these distinct segments, the system can identify spoofing conditions through the residual data without requiring complete redesign of the entire GNSS processing chain, thus improving reliability while managing complexity.
Solution Approach 2:
The patent introduces predicted correlation data as an intermediary element that serves as a reference for comparing against real-time correlation data. This intermediary allows the system to detect spoofing by identifying discrepancies between expected and actual signals, enabling reliable spoofing detection without directly modifying the fundamental GNSS signal processing architecture.
2Measurement precision
If the system monitors all correlation data peaks, then spoofing detection accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary generation of predicted correlation data based on historical and expected signal patterns before comparing with real-time data. This preliminary action allows the system to pre-establish what authentic peaks should look like, enabling faster real-time detection by simply comparing against the predicted pattern rather than analyzing all possible peaks from scratch, thus improving accuracy while reducing processing time.
Solution Approach 2:
The patent focuses monitoring efforts on specific critical aspects of the correlation data - particularly the residual correlation data after subtracting predicted from real-time data. Instead of exhaustively analyzing every aspect of the signal, the system concentrates computational resources on the most informative partial data set, achieving high detection accuracy with reduced overall processing requirements.
3Reliability
If the system uses simple peak detection, then the device complexity remains low, but the system cannot reliably identify authentic peaks among multiple peaks caused by spoofing
Solution Approach 1:
The patent extracts the residual correlation data by subtracting predicted correlation data from real-time correlation data. This extraction isolates the anomalies and discrepancies that indicate spoofing, allowing the system to focus analysis on the extracted residual data rather than attempting to identify authentic peaks directly within the complex mixture of authentic and counterfeit signals, thereby improving identification reliability while managing analytical complexity.
Solution Approach 2:
The patent transforms the peak identification problem by changing the parameter space from analyzing absolute correlation values to analyzing residual differences between predicted and actual values. This parameter transformation converts the difficult problem of identifying authentic peaks among multiple peaks into the more tractable problem of detecting anomalies in residual data, improving reliability without proportionally increasing complexity.
Data Source
AI summary
Systems, methods, and non-transitory computer program products for mitigating global navigation satellite system spoofing (GNSS). Real-time correlation data derived from a GNSS signal is received from a tracking channel over a time period. The real-time correlation data includes one or more peaks. The one or more peaks of the real-time correlation data are monitored to determine a presence of at least two distinct peaks separated by at least a predetermined minimum amount of time. An authentic peak is identified within the at least two distinct peaks based on a comparison of predicted correlation data with the real-time correlation data. A tracking command is provided to the tracking channel to facilitate modifying tracking points to center on the authentic peak.