Hybrid Navigation Spoofing Detection Using Sustained Deviations
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Solution Overview
Problem
Hybrid navigation systems are vulnerable to spoofing attacks, where fraudulent satellite signals deceive the navigation system, leading to inaccurate position data and compromising navigation accuracy.
Innovation Solution
A method that continuously computes inertial and satellite locations, calculates deviations, and uses a statistical error model to detect spoofing by comparing deviations against thresholds, allowing for rapid and slow spoofing detection with minimal computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hybrid navigation uses satellite location data to correct inertial navigation, then navigation accuracy is improved, but the system becomes vulnerable to spoofing attacks that can deceive the navigation system
Solution Approach 1:
The patent introduces an intermediary detection mechanism that sits between the satellite navigation system and the navigation computation unit. This intermediary layer monitors for spoofing conditions and prevents fraudulent satellite signals from corrupting the hybrid navigation solution, thereby maintaining both accuracy and security.
Solution Approach 2:
The system continuously monitors the navigation solution for signs of spoofing and uses this feedback to adjust or reject satellite-based corrections when fraud is detected. This feedback loop allows the system to maintain navigation accuracy while protecting against spoofing attacks by adapting to changing signal conditions.
2Reliability
If the system monitors deviations continuously to detect spoofing, then detection capability is improved, but computational resources and processing time increase
Solution Approach 1:
Instead of continuously monitoring all possible deviation parameters, the patent applies partial action by focusing monitoring on specific deviation thresholds and patterns that are most indicative of spoofing. This selective monitoring approach maintains detection capability while significantly reducing computational resource consumption.
Solution Approach 2:
The system pre-establishes deviation thresholds and monitoring criteria before spoofing occurs. By having these detection parameters pre-configured, the system can quickly identify spoofing conditions without performing complex real-time calculations, thus reducing computational burden while maintaining effective detection.
Data Source
AI summary
A navigation method includes continuously computing an inertial location and, at successive current times, obtaining a satellite location in order to compute a hybridised location by hybridising the inertial location and the satellite location. The method further includes: computing an instantaneous deviation at a given time and then a deviation that has been sustained from the computation time of said instantaneous deviation up to the current time in order to have, for a given period of time, a plurality of sustained deviations from different computation times up to the same current time; computing, for each sustained deviation, at least one detection indicator; comparing said detection indicator with a threshold in order to detect a deficiency of the hybridised location at the current time.