GNSS Spoofing Detection via Sparse Waveform Decomposition
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Solution Overview
Problem
Existing GNSS receivers lack effective methods to detect spoofing attacks, which can deceive receivers into locking onto counterfeit signals, leading to flawed position, velocity, and timing solutions.
Innovation Solution
The system employs a processor and a dictionary of pre-computed discrete-time waveforms to identify a sparse combination of components in the output signal of a correlator bank, using sparse optimization algorithms like LASSO to distinguish between intended and spoofing signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If commercial off-the-shelf (COTS) receivers are used, then cost is reduced and broad applications are enabled, but ability to detect spoofing attacks is lost
Solution Approach 1:
The system pre-computes and stores discrete-time waveform functions representing expected authentic GNSS signals in a lookup table before signal reception. During operation, the receiver compares incoming signals against these pre-computed waveforms to quickly identify spoofing attacks, eliminating the need for complex real-time signal generation and enabling detection in cost-effective COTS receivers.
2Reliability
If spoofing detection algorithms are implemented to identify counterfeit signals, then spoofing detection capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system pre-computes and stores discrete-time waveform functions representing expected authentic GNSS signals in a lookup table before signal reception. During operation, the receiver compares incoming signals against these pre-computed waveforms to quickly identify spoofing attacks, eliminating the need for complex real-time signal generation and enabling detection in cost-effective COTS receivers.
Solution Approach 2:
The system creates simplified representations (copies) of authentic GNSS signals in the form of discrete-time waveform functions stored in a lookup table. These waveform copies capture the essential characteristics of authentic signals without requiring full signal reconstruction, enabling efficient comparison and detection while reducing computational complexity.
3Measurement precision
If correlation analysis is performed on received signals to identify signal components, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system pre-computes and stores discrete-time waveform functions representing expected authentic GNSS signals in a lookup table before signal reception. During operation, the receiver compares incoming signals against these pre-computed waveforms to quickly identify spoofing attacks, eliminating the need for complex real-time signal generation and enabling detection in cost-effective COTS receivers.
Data Source
AI summary
A global navigation satellite system (GNSS) spoofing detection and classification technique is provided. An optimization problem is formulated at the baseband correlator domain by using an optimization algorithm such as the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, for example. A model of correlator tap outputs of the intended received signal is created to form a dictionary of pre-computed waveform functions (e.g., triangle-like-shaped functions). Sparse signal processing can be leveraged to choose a decomposition of pre-computed waveform functions from the dictionary. The optimal solution of this minimization problem can discriminate the presence of a potential spoofing attack peak by observing the decomposition of two different code-phase values (authentic and spoofed) in a sparse vector output. A threshold can be used to mitigate false alarms. Furthermore, a variation of the minimization problem can be provided that enhances the dictionary to a higher resolution.