GNSS Clock Offset Residual Monitoring for Spoofing Detection
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Solution Overview
Problem
GNSS receivers are vulnerable to spoofing attacks, which cause incorrect position solutions and pose risks in aviation and other applications by introducing unbounded position errors, especially in conflict-prone regions, without triggering traditional monitoring mechanisms.
Innovation Solution
A method involving a moving window curve fit analysis of GNSS clock offset residuals is employed to detect spoofing by identifying jumps in clock error that exceed a predetermined threshold, using a curve fit procedure to monitor clock offset behavior over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring mechanisms are used, then system simplicity is maintained, but spoofing detection capability is insufficient
Solution Approach 1:
The patent extracts the clock offset parameter from the complete GNSS solution and isolates it for specialized analysis. By focusing monitoring efforts on this specific parameter through curve fit procedures, the system achieves enhanced spoofing detection without requiring complete redesign of the monitoring architecture.
Solution Approach 2:
The patent performs curve fit procedures on clock offset data in advance, storing the fitted values and residuals for later comparison. This preliminary analysis enables rapid spoofing detection when anomalies occur, as the baseline behavior is already established through pre-computed curve fits.
2Measurement precision
If curve fit procedures are performed on clock offset, then spoofing detection accuracy is improved, but computational processing time increases
Solution Approach 1:
The patent applies curve fit procedures selectively to clock offset data rather than processing all GNSS solution parameters. This partial action approach focuses computational resources on the most indicative parameter for spoofing detection, achieving high accuracy while minimizing overall processing time.
Solution Approach 2:
The system pre-computes curve fits for clock offset data and stores them for rapid comparison. By performing the computationally intensive curve fitting in advance and storing results, the system enables fast real-time spoofing detection without repeating full computational procedures.
3Reliability
If clock offset monitoring is implemented, then position integrity is maintained, but system complexity increases
Solution Approach 1:
The patent extracts and monitors only the clock offset parameter from the GNSS solution, rather than implementing comprehensive monitoring of all position and navigation parameters. This selective approach maintains position integrity through focused surveillance while avoiding the complexity of全方位的 monitoring systems.
Solution Approach 2:
The curve fit procedure acts as an intermediary that transforms raw clock offset data into meaningful anomaly indicators. By introducing this intermediate processing step, the system translates complex temporal variations into simple residual comparisons that can trigger alerts without requiring complex decision logic.
Data Source
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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.