A Deep Neural Network Approach for Classification of GNSS Interference and Jammer

Global Navigation Satellite Systems (GNSS) are one of the most important infrastructures in the modern world for positioning and timing, also enabling many critical applications that require the reliability of the received signals. However, it is well known that the power of the GNSS signals at the receiver's antenna is extremely weak, and radio-frequency interference affecting the GNSS bandwidths might lead to reduced positioning and timing accuracy or even a complete lack of the navigation solution. Therefore, in order to mitigate interference in the GNSS receivers and guarantee reliable solutions, interference classification becomes of paramount importance. This paper proposes an approach for the automatic and accurate classification of the most common interference and jammers based on the use of Convolutional Neural Networks (CNN). The input for the network is the time-frequency representation of the received signal, together with features in the time and frequency domains. The time-frequency representation is obtained using both the Wigner-Ville and the short-time Fourier transforms. Moreover, the performance of the proposed method is compared using two different CNN architectures, AlexNet and ResNet. The effectiveness of the method is shown in two case studies: Monitoring and classification by a terrestrial station and from a Low Earth Orbit (LEO) satellite. The results reveal that the proposed method achieves a high accuracy of 99.69% in classifying interference, even with low interference power, and can be implemented as a real-time tool for monitoring jammers.