Cellular Base Station GNSS Monitoring for Spoofing Localization
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Solution Overview
Problem
Existing systems are vulnerable to GPS jamming and spoofing attacks, which compromise the accuracy and reliability of GNSS signals, particularly affecting autonomous and safety-critical applications like autonomous driving and aviation, without effective area-wide detection methods.
Innovation Solution
Utilize stationary GNSS receivers integrated into cellular network infrastructure, such as base stations, to continuously monitor GNSS signals, comparing historical data with live data for anomalies, and implement machine learning to detect and localize spoofing and jamming attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stationary GNSS receivers are deployed in cellular networks for continuous monitoring, then detection coverage and reliability are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The patent reuses existing cellular network infrastructure (base stations with integrated GNSS receivers) for dual purposes: original cellular communication functions and GNSS anomaly detection. This multi-functionality approach improves detection reliability without requiring entirely new dedicated detection infrastructure, thereby limiting the increase in system complexity.
Solution Approach 2:
The system uses the cellular network's own existing resources (base station GNSS receivers, data communication interfaces, and processing capabilities) to perform self-diagnosis and anomaly detection. This self-service approach enables reliable detection while avoiding additional external infrastructure requirements.
2Measurement precision
If historical GNSS data is stored and compared with live data for anomaly detection, then detection accuracy is improved, but data storage requirements and processing time increase
Solution Approach 1:
The system stores and compares only essential GNSS parameters (position coordinates, timing information, signal strength) rather than complete raw signal data. This partial action approach maintains sufficient detection accuracy by focusing on key anomaly indicators while reducing storage requirements and processing time.
Solution Approach 2:
The system pre-stores baseline GNSS measurement data from stationary receivers and pre-establishes anomaly detection thresholds. When live data arrives, comparison is immediately performed against pre-prepared reference data, enabling rapid detection without extensive real-time processing.
3Measurement precision
If machine learning algorithms are implemented for anomaly detection, then detection capability and localization accuracy are improved, but computational requirements and energy consumption increase
Solution Approach 1:
The machine learning-based detection system is segmented into distributed components at cellular base stations and a centralized analysis server. Base stations perform local preprocessing and feature extraction using lightweight algorithms, while complex model training and sophisticated pattern recognition occur centrally. This segmentation reduces energy consumption at distributed receivers while maintaining high localization accuracy through centralized computational resources.
Data Source
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AI summary
Provided are a method and a system for detecting position measurement anomalies in a global navigation satellite system (GNSS) for a plurality of stationary GNSS receivers. Therein, GNSS measurement indication from a GNSS receiver is determined to be affected by an anomaly if the GNSS measurement indication is not received at a configured timing, if the measured GNSS signal parameter value is invalid or deviates from a recorded position of the GNSS receiver or from a recorded GNSS signal parameter value from a past measurement by the stationary GNSS receiver. Accordingly, the present disclosure facilitates cost efficient, areawide, high-resolution, and continuous monitoring of GNSS anomalies (such as spoofing or jamming attacks).