UAV Navigation Spoofing Detection via Signal Strength Variance
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Solution Overview
Problem
Unmanned vehicles, such as UAVs, face challenges in determining the trustworthiness of navigation data, particularly GPS data, which can be spoofed by external sources, leading to potential navigation errors and hijacking risks during autonomous operations.
Innovation Solution
Implementing various techniques to detect spoofed GPS data, including comparing GPS data with internally generated navigation data from sensors, using imaging sensors for image recognition, establishing a web of trust with other UAVs, evaluating signal strength based on environmental conditions, and analyzing historical navigation data to determine the reliability of GPS signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used for autonomous navigation, then navigation accuracy is improved, but vulnerability to spoofing attacks increases
Solution Approach 1:
The system continuously monitors GPS signal characteristics including signal strength, carrier-to-noise ratio, and signal arrival time. This feedback mechanism compares expected signal parameters with actual received parameters to detect anomalies that may indicate spoofing attacks, allowing the system to maintain navigation accuracy while identifying unreliable GPS data
Solution Approach 2:
The patent introduces an intermediary verification layer that acts as a mediator between GPS data reception and navigation decision-making. This intermediary system cross-checks GPS signals against multiple criteria including signal consistency, environmental context, and historical data patterns before accepting GPS data for navigation, thereby filtering out spoofed signals while preserving legitimate navigation accuracy
2Reliability
If multiple verification techniques are implemented, then detection capability is improved, but system complexity increases
Solution Approach 1:
The verification system is segmented into distinct functional modules: signal strength monitoring module, carrier-to-noise ratio analysis module, signal arrival time verification module, and contextual environment assessment module. Each module independently evaluates specific aspects of GPS signal validity, and their results are combined to make overall spoofing detection decisions, reducing system complexity through functional decomposition
Solution Approach 2:
The system implements verification techniques selectively based on risk assessment and operational context. Not all verification methods are applied continuously at full intensity; instead, the system adjusts the level of verification based on factors such as flight phase, environmental conditions, and detected anomaly levels, reducing overall system complexity while maintaining adequate detection capability
Data Source
AI summary
Techniques for determining whether data associated with an autonomous/non-autonomous operation of a manned/unmanned vehicle may be trusted. For example, a first set of data may be provided from a source external to a manned/unmanned vehicle. A second set of data may be accessed. This second set may be provided from a source internal or external to the manned/unmanned vehicle and may be associated with the same autonomous/non-autonomous operation. The two sets may be compared to determine whether the first set of data may be trusted or not. If untrusted, a corrective action may be performed.