No abstract

<p>Global Navigation Satellite Systems (GNSS) are one of the most important infrastructures in the modern world, 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 detection and classification becomes of paramount importance. This paper proposes an approach for the automatic and accurate detection and classification of the most common interference and jammers based on the use of Convolutional Neural Networks (CNN). The input for the network is the visual time-frequency representation of the received signal, together with statistical 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 monitoring station and from a Low Earth Orbit satellite (LEO). The results show that the proposed method has quite a high accuracy in detecting and classifying interference, even with low power, and can be implemented as a real-time tool for monitoring jammers.</p>

<p>Global Navigation Satellite Systems (GNSS) are one of the most important infrastructures in the modern world, 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 detection and classification becomes of paramount importance. This paper proposes an approach for the automatic and accurate detection and classification of the most common interference and jammers based on the use of Convolutional Neural Networks (CNN). The input for the network is the visual time-frequency representation of the received signal, together with statistical 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 monitoring station and from a Low Earth Orbit satellite (LEO). The results show that the proposed method has quite a high accuracy in detecting and classifying interference, even with low power, and can be implemented as a real-time tool for monitoring jammers.</p>

<p>Signal monitoring and recording station architectures based on software-defined radio (SDR) have been proposed and implemented since several years. However, the large amount of data to be transferred, stored, and managed when high sampling frequency and high quantization depth are required, poses a limit to the performance, mostly because of the data losses during the data transfer between the front-end and the storage unit. To overcome these limitations, thus allowing a reliable, high-fidelity recording of the signals as required by some applications, a novel architecture named SMART (Signal Monitoring, Analysis and Recording Tool) based on the implementation of Docker containers directly on a Network Attached Storage (NAS) unit is presented. Such paradigms allow for a fully open-source system being more affordable and flexible than previous prototypes. The proposed architecture reduces computational complexity, increases efficiency, and provides a compact, cost-effective system that is easy to move and deploy. As a case study, this architecture is implemented to monitor RFIs on Global Navigation Satellite System (GNSS) L1/E1 and L5/E5 bands. The sample results show the benefits of a stable, long-term capture at a high sampling frequency to characterize the RFIs spectral signature effectively. </p>

<p>Signal monitoring and recording station architectures based on software-defined radio (SDR) have been proposed and implemented since several years. However, the large amount of data to be transferred, stored, and managed when high sampling frequency and high quantization depth are required, poses a limit to the performance, mostly because of the data losses during the data transfer between the front-end and the storage unit. To overcome these limitations, thus allowing a reliable, high-fidelity recording of the signals as required by some applications, a novel architecture named SMART (Signal Monitoring, Analysis and Recording Tool) based on the implementation of Docker containers directly on a Network Attached Storage (NAS) unit is presented. Such paradigms allow for a fully open-source system being more affordable and flexible than previous prototypes. The proposed architecture reduces computational complexity, increases efficiency, and provides a compact, cost-effective system that is easy to move and deploy. As a case study, this architecture is implemented to monitor RFIs on Global Navigation Satellite System (GNSS) L1/E1 and L5/E5 bands. The sample results show the benefits of a stable, long-term capture at a high sampling frequency to characterize the RFIs spectral signature effectively. </p>