Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. Here, literature proposes ResNet18 and TS-Transformer as they provide the most accurate classification rates on quasi-realistic GNSS signals. However, employing these methods individually, they only focus on either spatial or temporal information and discard information during optimization, thereby degrading classification accuracy. This paper proposes a deep learning framework that considers both the spatial and temporal relationships between samples when fusing ResNet18 and TS-Transformers with a joint loss function to compensate for the weaknesses of both methods considered individually. Our real-world experiments show that our novel fusion pipeline with an adapted late fusion technique and uncertainty measure significantly outperforms the state-of-the-art classifiers by 6.7% on average, even in complicated realistic scenarios with multipath propagation and environmental dynamics. This works even well (F-?=2 score about 80.1%), when we fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor. By using late fusion the classification accuracy of the classes FreqHopper, Modulated, and Noise increases while lowering the uncertainty of Multitone, Noise, and Pulsed. The improved classification capabilities allow for more reliable results even in challenging scenarios.

Global navigation satellite system (GNSS) receivers use tracking loops to lock onto GNSS signals. Fixed loop settings limit the tracking performance against noise, receiver dynamics, and the current scenario. Adaptive tracking loops adjust these settings to achieve optimal performance for a given scenario. This paper evaluates the performance and complexity of state-of-the-art adaptive scalar tracking techniques used in modern digital GNSS receivers. Ideally, a tracking channel should be adjusted to both noisy and dynamic environments for optimal performance, defined by tracking precision and loop robustness. The difference between the average tracking jitter of the discriminator’s output and the square-root Cramér-Rao bound (CRB) indicates the loops’ tracking capability. The ability to maintain lock characterizes the robustness in highly dynamic scenarios. From a system perspective, the average lock indicator is chosen as a metric to measure the performance in terms of precision, whereas the average number of visible satellites being tracked indicates the system’s robustness against dynamics. The average of these metrics’ product at different noise levels leads to a reliable system performance metric. Adaptive tracking techniques, such as the fast adaptive bandwidth (FAB), the fuzzy logic (FL), and the loop-bandwidth control algorithm (LBCA), facilitate a trade-off for optimal performance. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. All three methods steer a third-order adaptive phase locked loop (PLL) and are tested in simulated scenarios emulating static and high-dynamic vehicular conditions. The measured tracking performance, system performance, and time complexity of each algorithm present a detailed analysis of the adaptive techniques. The results show that the LBCA with a piece-wise linear approximation is above the other adaptive loop-bandwidth tracking techniques while preserving the best performance and lowest time complexity. This technique achieves superior static and dynamic system performance being 1.5 times more complex than the traditional tracking loop.

Interference signals degrade the performance of a global navigation satellite system (GNSS) receiver. Classification of these interference signals allow better situational awareness and facilitate appropriate counter-measures. However, classification is challenging and processing-intensive, especially in severe multipath environments. This article proposes a low-resource interference classification approach that combines conventional statistical signal processing approaches with machine learning (ML). It leverages the processing efficiency of conventional statistical signal processing by summarizing, e.g., a short-time Fourier transform (STFT), with statistical measures. Furthermore, the ML design space is bounded as the signal is pre-processed. It results in fewer opportunities for ML but facilitates faster convergence and the use of simpler architectures. Therefore, this approach has lower ML training complexity and lower processing and memory requirements. Results show competitive classification capabilities to more complex approaches. It demonstrates that more efficient architectures can be developed using existing signal-processing approaches

This paper analyzes the signals of different commercial-off-the-shelf (COTS) privacy protection devices (PPDs), and compares several mitigation algorithms applied to these jamming signals. A review of the mitigation algorithms is presented, including the theoretical requirements. This paper allows an end-to-end comparison of the algorithms in terms of the final performance versus the implementation requirements. The properties of the interference signal, specifically the dynamics of the signal, greatly affects the mitigation capability. For high interference-to-noise ratio (INR), the filter-bank pulse blanking (FBPB) had the best performance followed by the Dual-frequency-domain adaptive filtering (FDAF). It was also shown that the modulation of the satellite signal selection has a significant influence on the performance.

Interference signals degrade global navigation satellite system (GNSS) performance and must be mitigated. Chirp signals can be mitigated with an adaptive notch filter (ANF), but the dynamic behavior limits performance. An ANF determines the instantaneous frequency and removes interference with a notch filter. However, there are several limitations. In this article, we propose a multi-parameter adaptive notch filter (MPANF) approach that significantly enhances conventional ANFs. First, it uses an loop-bandwidth control algorithm (LBCA) to alter the loop bandwidth of an frequency-locked loop (FLL)-based adaptation algorithm to facilitate superior tracking agility-to-precision trade-off. Second, it dynamically adjusts the notch depth to switch on interference mitigation or pass the signal through. Third, it modifies the notch width to accommodate tracking stability and optimize interference signal suppression to GNSS signal removal. The presented MPANF exhibits superior performance against chirp signals, including faster response to jump discontinuities.