GNSS spoofing is a type of intentional interference in which a GNSS receiver tracks counterfeit signals making the outcome positions wrong. This is the most dangerous type of intervention in GNSS technology. In this work, we introduce a novel method to detect spoofing signals using Gaussian Mixture Model (GMM) on the double carrier phase difference (DD) created by two independent receivers. The DD is capable of completely eliminating errors caused by the satellite clock, the receiver clock, the atmospheric layers; therefore, the signal Angle of Arrival (AoA) is clearly expressed in the DD measurement. We utilize the GMM to model the probability density function of the DD measurement, which is computed from the phase measurement of the receivers. Theoretically, AoA values of an authentic signal change over time due to the nature of the signal broadcasted from an orbiting satellite. On the other hand, fake signals are often transmitted from a generator causing a central distribution of the corresponding AoA values. Existing work deals with the spoofing detection problem using the above theoretical assumption. However, it is practically to broadcast spoofing signals from several sources, and it is possible to mix up some random noise into the generated phase to make the DD measurement noisy. In such a complicated scenario, the existing approaches are not robust enough to detect non-authentic signals. We have another observation that, since the real satellites are moving on fixed orbits, there should be a correlation among the AoA values of signals coming from such real ones. In contrast, counterfeit signals (with the main purpose of causing wrong position or noisy phase) do not follow the pattern of the real ones. Therefore, instead of using the above hard assumption about the signal AoA, we propose to make use of statistical model GMM to learn the hidden relationship of the AoA values among real signals coming from different real satellites and then those GMM models are used to detect spoofing signals.
Due to unlimited increase of cars and transportation systems, a real time embedded system called Automatic Number Plate Recognition (ANPR) is very important for humans to detect and manage. This paper presents results of developing and deploying an ANPR applied to electronic tolling collection (ETC) systems in Vietnam with some special issues. Our model is designed and investigated by using a VIVOTEK IP8361 camera to capture an image. After that, the image is transmitted to an industrial computer to process. In detail, the image is processed first to reduce noise and artifacts by a low-pass filter before our software detects plate candidates. The characters in the candidates are then extracted by an optical character recognition utilizing neural network. We also employ Microsoft visual C sharp integrated development environment to build graphical user interface. Experimental results manifest the high accuracy of our method achieving approximately 85.00% and the processing time of only about 20-30ms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.