GNSS Jamming Classification via CNN, Transfer Learning the Novel Concatenation of Signal Representations

摘要

RF connectivity is pervasive in many systems to- day and can underpin fundamental services. Intentional Global Navigation Satellite System (GNSS) jamming activities are increasing across the globe, causing significant threats to real life applications from power distribution to finance and even 5G performance. The first step towards its mitigation is the detection and classification of the signal. Classification could inform an attribution picture. For example, connecting a perpetrator through the jamming signal type from a device found in their possession. This paper introduces a novel approach which utilises transfer learning from the imagery domain and considers the jamming signal power spectral density (PSD), spectrogram, raw constellation, and histogram signal representations as images. Collecting datasets large enough to train a neural network from scratch is a common problem. The use of Transfer Learning overcomes this issue. Transfer learning is applied through feature extraction using a Convolutional Neural Network (CNN) VGG16 pretrained on the ImageNet dataset. Various Machine Learning classifiers are evaluated including Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). To date, prior research in this field has concentrated on spectrogram representation but our evidence shows that the novel concatenation of signal representations (PSD, spectrogram, raw constellation and histogram) is more effective, allowing the CNN to benefit from the strengths of each individual representation. The image concatenation dataset produced 98% (+/- 0.5%) classification accuracy with LR and SVM models and 96.3% (+/- 0.6%) with RF. The results, validated through 10-fold cross validation, showed that transfer learning using CNN VGG16 in conjunction with ML models LR, SVM, and RF and the concatenation of signal representations, produces high accuracy for the classification of GNSS jamming signals and outperforms previous work in the field.