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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-β=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.

Terraform module for VMWare VCenter instanace

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference needs to be detected, classified, its purpose determined, and localized, such that it can be eliminated. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This paper 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-possessing 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 to be deployed, resulting in a significantly higher probability of intercept (POI). The initial results of controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches.

Apache CloudStack Terraform VM abstraction