GNSS Spoofing Detection via IMU Barometer Correlation
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Solution Overview
Problem
GNSS receivers are vulnerable to spoofing attacks, where malicious users can manipulate or introduce counterfeit signals, making it difficult to detect illegitimate readings, especially when the antenna is shielded or exposed, and existing detection mechanisms are ineffective.
Innovation Solution
A system and method that utilize a processor to receive and process GNSS data alongside IMU and barometer data, correlating acceleration, angular velocity, and height variation data to calculate correlation coefficients and a decision metric, determining if GNSS data is spoofed by comparing it against a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS antenna is exposed for signal reception, then GNSS positioning function is achieved, but the system becomes vulnerable to spoofing attacks
Solution Approach 1:
The patent introduces an intermediary detection system that uses IMU and barometer sensors as mediators to verify the authenticity of GNSS signals. These sensors provide independent measurement data that acts as a reference to detect inconsistencies caused by spoofing attacks, without requiring modification to the GNSS antenna or signal reception process.
Solution Approach 2:
The system implements feedback by continuously monitoring and comparing data from multiple sensors (GNSS, IMU, barometer) to detect inconsistencies. When spoofing is detected through correlation analysis, the system can trigger alerts or switch to alternative positioning methods, providing real-time feedback to maintain positioning reliability.
2Measurement precision
If multiple sensors are integrated for spoofing detection, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent makes existing sensors (IMU and barometer) serve dual functions: their primary navigation/monitoring functions continue unchanged, while they simultaneously provide data for spoofing detection. This multi-functionality approach improves detection accuracy without requiring dedicated spoofing detection hardware, thereby limiting complexity increase.
Solution Approach 2:
The system uses the vehicle's existing sensor infrastructure (IMU and barometer already present for other purposes) to perform spoofing detection. The sensors essentially serve themselves by providing both their primary function data and spoofing detection data, eliminating the need for additional dedicated detection sensors and reducing overall system complexity.
3Reliability
If correlation analysis is performed on multiple sensor data streams, then spoofing detection capability is enhanced, but processing time increases
Solution Approach 1:
The patent applies partial action by selectively analyzing specific parameters from sensor data streams that are most indicative of spoofing (e.g., acceleration correlations, height variations). Rather than processing all available sensor data equally, the system focuses computational resources on the most discriminative features, enhancing detection capability while limiting processing time increase.
Data Source
AI summary
Systems and methods for detecting spoofed or illegitimate GNSS signals. A processor receives GNSS data and processes this data to extract acceleration, angular velocity, and height or altitude variation data. For the same time period, sensor data from IMU (inertial measurement unit) sensors and from a barometer are received by the processor. From the sensor data, the processor extracts similar acceleration, angular velocity, and height variation data. These two sets of data are then correlated and correlation coefficients are calculated. These correlation coefficients are then used to calculate a decision statistic. The decision statistic is compared with a predetermined value and, if the decision statistic is below a predetermined value, then the GNSS data is considered to be illegitimate or spoofed.