Interpretable Rule-Based Architecture for GNSS Jamming Signal Classification

Abstract

Jamming is a fatal threat to a global navigation satellite system (GNSS), and an efficient anti-jamming system relies on successful classification and identification of jamming types to respond effectively. The existing solutions suffer either from poor accuracy or lack of interpretability, and they are prone to learning simple statistical correlations rather than more fundamental and general relationships. In this study, we propose a novel approach to classify GNSS jamming signals as intentional or unintentional. The approach introduces a new standard deviation-based denoising method, which makes it possible to use the logical rule-based architecture of the convolutional Tsetlin machine (CTM) for interpretable jamming signal analysis. CTM is a recently developed algorithm that solves complex classification problems using conjunctive propositional formulas through a team of Tsetlin automata (TA). Unlike traditional opaque models based on deep learning, our approach goes beyond classification and provides a human-level interpretation of features. This interpretation capability allows a deeper comprehension of the characteristics and underlying patterns of the jamming signals, significantly easing the decision-making process. Furthermore, the CTM approach is also evaluated with different Booleanization techniques. Through experiments, we show that the proposed approach with CTM achieves an F1-score of 98.7%, on the collected dataset, which is superior to the state-of-the-art.