GNSS Multipath Detection Using Embedded Deep CNN on Intel® Neural Compute Stick

Munin, Evgenii; Blais, Antoine; Couellan, Nicolas

doi:10.33012/2020.17654

Cited by 7 publications

(7 citation statements)

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“…This could lead the method to be more implementable on real-time receivers so that the number of computed correlators is more achievable. Even 3D images can be built by taking into account the Doppler offset of the received signal, as done by Munin et al (2020). Furthermore, other factors could be added for threshold determination such as the elevation of the satellite.…”

Section: Discussionmentioning

confidence: 99%

“…This model is a simple one; it was observed that a complex CNN was not needed to successfully detect multipath from the correlator images. Indeed, with respect to Munin et al (2020), who also use CNN for multipath detection, the presented network uses one less convolutional layer, making it more lightweight. Furthermore, special attention was given to overfitting by implementing dropout and L1 and L2 regularization, while Munin et al (2020) and Quan et al (2018) do not mention this threat.…”

Section: Proposed Architecturementioning

confidence: 99%

“…Indeed, with respect to Munin et al (2020), who also use CNN for multipath detection, the presented network uses one less convolutional layer, making it more lightweight. Furthermore, special attention was given to overfitting by implementing dropout and L1 and L2 regularization, while Munin et al (2020) and Quan et al (2018) do not mention this threat. As multipath and the filter can introduce correlation between data inputs, overfitting can be even more prominent when using post-tracking outputs.…”

Section: Proposed Architecturementioning

confidence: 99%

“…Lately, machine learning (ML) has been applied to GNSS data processing especially for multipath detection/mitigation. For example Munin et al (2020) and Blais et al (2022) fed synthetic correlator outputs to a convolutional neural network (CNN) to detect multipath. Since they generated controlled multipath and knew when multipath affected the correlators, their method lacked a classification process that made the method applicable to real signals.…”

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confidence: 99%

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Using convolutional neural networks to detect GNSS multipath

2023

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Global Navigation Satellite System (GNSS) multipath has always been extensively researched as it is one of the hardest error sources to predict and model. External sensors are often used to remove or detect it, which transforms the process into a cumbersome data set-up. Thus, we decided to only use GNSS correlator outputs to detect a large-amplitude multipath, on Galileo E1-B and GPS L1 C/A, using a convolutional neural network (CNN). This network was trained using 101 correlator outputs being used as a theoretical classifier. To take advantage of the strengths of convolutional neural networks for image detection, images representing the correlator output values as a function of delay and time were generated. The presented model has an F score of 94.7% on Galileo E1-B and 91.6% on GPS L1 C/A. To reduce the computational load, the number of correlator outputs and correlator sampling frequency was then decreased by a factor of 4, and the convolutional neural network still has an F score of 91.8% on Galileo E1-B and 90.5% on GPS L1 C/A.

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Section: Discussionmentioning

confidence: 99%

Section: Proposed Architecturementioning

confidence: 99%

Section: Proposed Architecturementioning

confidence: 99%

mentioning

confidence: 99%

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Using convolutional neural networks to detect GNSS multipath

2023

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“…Additionally, there are hardware-based detection methods that use a special antenna [50], a sky-pointing camera [51], and a method that uses a three-dimensional (3D) model of a city and ray-tracing techniques [52]. Furthermore, GNSS multipath signal detection techniques using machine learning approaches have been emerging recently [53]- [55].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

Kim,

Byun,

Park

2022

Preprint

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In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global positioning system (GPS) multipath detection that uses dual antennas. A machine learning model that could classify GPS signal reception conditions was trained with several GPS measurements selected as suggested features. We applied five features for machine learning, including a feature obtained from the dual antennas, and evaluated the classification performance of the model, after applying four machine learning algorithms: gradient boosting decision tree (GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was found that a classification accuracy of 82%-96% was achieved when the test data set was collected at the same locations as those of the training data set. However, when the test data set was collected at locations different from those of the training data, a classification accuracy of 44%-77% was obtained.

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A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

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GNSS Multipath Detection Using Embedded Deep CNN on Intel® Neural Compute Stick

Cited by 7 publications

References 0 publications

Using convolutional neural networks to detect GNSS multipath

Using convolutional neural networks to detect GNSS multipath

Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

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