GNSS Clock Offset Curve Fitting for Spoofing Detection
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Solution Overview
Problem
GNSS receivers are vulnerable to spoofing attacks, which cause incorrect position solutions and pose a significant threat to commercial, military, and civilian applications, particularly in conflict-prone regions, leading to potential safety hazards.
Innovation Solution
A method involving a moving window curve fit technique is employed to monitor the clock offset of GNSS receivers, where the clock offset is placed into a time buffer and fitted with a curve to identify inconsistencies, with residuals exceeding a threshold indicating spoofing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional GNSS position calculation methods are used, then position solutions can be obtained, but the system becomes vulnerable to spoofing attacks causing incorrect position solutions
Solution Approach 1:
The patent introduces clock offset as an intermediary parameter to detect spoofing attacks. Instead of directly detecting position spoofing, the system monitors the clock offset derived from GNSS measurements, which serves as a mediator that reveals inconsistencies in spoofed signals. The clock offset is computed from the same measurements used for position but provides an independent verification channel that exposes spoofing without requiring additional hardware.
Solution Approach 2:
The system performs preliminary curve fitting on the clock offset data before using it for spoofing detection. By pre-processing the clock offset measurements through curve fitting and examining residuals, the system prepares the data in advance to identify anomalies. This preliminary action transforms raw clock offset data into a form that clearly reveals spoofing attempts, making the detection process more reliable and reducing the complexity of real-time analysis.
2Measurement precision
If clock offset monitoring is implemented to detect spoofing, then spoofing detection accuracy improves, but computational processing requirements increase
Solution Approach 1:
The patent applies partial curve fitting to a limited time window of clock offset data rather than processing all historical data. By fitting curves only to recent measurements within a moving window, the system achieves sufficient precision for spoofing detection while significantly reducing computational energy consumption. This partial action approach provides the necessary measurement accuracy without the excessive computational burden of analyzing the complete data history.
Solution Approach 2:
The system segments the clock offset data into moving time windows and processes each segment independently through curve fitting. This segmentation allows the system to maintain high measurement precision by focusing computational resources on recent, relevant data while reducing overall energy consumption by avoiding redundant processing of older data. Each window is processed separately, enabling efficient use of computational resources.
3Loss of time
If a moving window curve fit approach is used, then spoofing detection timeliness improves, but the complexity of data processing increases
Solution Approach 1:
The patent implements a dynamic moving window approach where the data window continuously moves forward in time, automatically discarding old data and incorporating new measurements. This dynamic structure enables timely spoofing detection by always analyzing the most recent data, reducing detection response time. The window-based approach naturally limits computational complexity by processing only a fixed, manageable number of recent points rather than accumulating unlimited historical data.
Solution Approach 2:
The system changes the parameter of data recency by using a moving window that prioritizes recent measurements over historical ones. This parameter change in data weighting and selection enables faster detection response by focusing on current signal conditions. The curve fitting parameters are applied to a dynamically updated subset of data, balancing processing complexity with detection timeliness by adjusting which data points receive computational attention.
Data Source
AI summary
A system comprises circuitry configured to: receive a plurality of GNSS signals; determine GNSS measurements based on the GNSS signals; compute a solution for position and clock offset based on the GNSS measurements; place the clock offset of the solution into a moving window time buffer having a time window; perform a curve fit procedure on the clock offset of the solution in the moving window time buffer to obtain a curve fit; compute curve fit residuals by subtracting the curve fit from the clock offset; examine magnitudes of the curve fit residuals over the time window to identify a largest curve fit residual of the curve fit residuals in the time window; determine whether the largest curve fit residual in the time window exceeds a predetermined detection threshold; and determine that spoofing is present when the largest curve fit residual in the time window exceeds the predetermined detection threshold.