Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach

Although the Global Navigation Satellite System (GNSS) technology provides an excellent benefit in different critical areas such as civilian, aviation, military, and commercial applications, it is highly vulnerable to various signal disruptions causing significant positioning errors. One of the major threats to a GNSS receiver is the intentional interference known as jamming. A Jammer significantly disrupts the normal functioning of a GNSS receiver, at the acquisition, tracking, and positioning stages. The foremost important step to combat against jamming of GNSS signals is the early detection and characterization of the interfering signals to guarantee the Quality of Service (QoS). This paper presents a robust Deep-Learning (DL) based technique using transfer learning to characterize the type of disruption in GNSS signal based on time-frequency analysis. To this end, a pre-trained Convolutional Neural Network (CNN) is used to extract the informative features from the scalogram of the received signals. Further, a fully connected layer followed by a Soft-Max activation function is deployed to classify the signals. In this work, the Signal of Interest (SoI) is a synthetic GPS signal generated by a GNSS simulator. In our experiment, the GPS signal is combined with different kinds of jamming, spoofing, and multipath signals. Moreover, the proposed classification approach can recognize not only the various kinds of jammers such as ones producing Continuous Wave Interference (CWI), Multi-CWI (MCWI), Chirp Interference (CI), and Pulse interference (PI) but also the inclusion of Additive White Gaussian Noise (AWGN). Besides that, the effect of five pre-trained CNNs, namely, AlexNet, GoogleNet, ResNet-18, VGG-16, and MobileNet-V2, is evaluated on classification accuracy. The GNSS signal and its seven disruptive variants are recorded at three different power levels such as low, medium, and high. The medium power level signal is used for training and the testing has been carried out for unseen data set of low, high, and mixed power level. From the simulation results, it has been observed that MobileNet-V2 has performed better than other techniques with an accuracy of 99. 8%. Finally, the trained MobileNet-V2 is used to predict the unseen data type generated at different Jamming to signal Ratios (JSRs).