GNSS Jamming Signal Detection via Spectrogram Analysis
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Solution Overview
Problem
Current GNSS jamming signal detection and classification methods are inadequate due to limitations in sensitivity, accuracy, and cost-effectiveness, as they rely on proprietary COTS receivers, SNR estimation, and expensive real-time spectrum analyzers, which are not practical for widespread application.
Innovation Solution
A method involving spectrogram analysis using a tree-based decision process to classify GNSS jamming signals, employing techniques such as spectral periodicity testing, power-based detection, and frequency variation analysis, which reduces noise and improves classification efficiency by distinguishing between different types of jamming signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If COTS GPS/GNSS receivers are used for RFI detection based on SNR or CN0 measurements, then the detection system can be implemented using commercially available components, but the detection sensitivity is reduced and detection delay increases due to filtering requirements
Solution Approach 1:
The patent performs RFI detection before the GNSS correlation stage, extracting features directly from the raw received signal. This preliminary detection approach avoids the sensitivity loss and delay inherent in post-correlation SNR/CN0 estimation, while still using standard COTS receiver hardware for the actual GNSS processing.
2Adaptability or versatility
If SNR or CN0 measurements are used for jamming detection, then the detection can be performed using standard receiver outputs, but the detection criteria must be loosened to tolerate variations in proprietary COTS receiver designs
Solution Approach 1:
The patent segments the detection process into multiple independent feature extractions from the raw signal (power spectral density, cyclic autocorrelation, spectrogram features) rather than relying on a single SNR/CN0 measurement. This multi-feature approach makes the detection more robust to variations in COTS receiver implementations while maintaining higher accuracy.
3Measurement precision
If a COTS real-time spectrum analyser is used for GNSS jamming signal detection, then better sensitivity and spectrum analysis capability are achieved, but the system becomes very expensive and overly sophisticated for the intended application
Solution Approach 1:
The patent extracts only the specific spectral features needed for RFI detection (power spectral density, cyclic autocorrelation at specific lags, spectrogram characteristics) from the received signal, rather than performing full real-time spectrum analysis. This selective feature extraction achieves the necessary sensitivity using standard GNSS receiver hardware without requiring expensive spectrum analyzers.
4Loss of information
If full real-time spectrum analysis is performed to characterize RFI, then detailed classification information is obtained, but the processing complexity and computational requirements increase significantly
Solution Approach 1:
The patent focuses computational resources on analyzing specific local features of the signal spectrum that are most indicative of RFI (such as cyclic autocorrelation at specific lags corresponding to GNSS code periods, and localized spectrogram patterns). This targeted analysis provides sufficient classification information without the computational burden of analyzing the entire spectrum in detail.
Data Source
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AI summary
Methods and apparatus for detecting, geo-locating and/or classifying a GPS or GNSS jamming signal are disclosed. One such method comprises the steps of analysing a spectrogram of the jamming signal using a tree-based decision process and as a result, then selecting one of a numberof types of jamming signal, wherein one of the decisions (1103) in the tree is whether the spectrogram has spectral periodicity.