Machine Learning Utilization in GNSS—Use Cases, Challenges and Future Applications

Siemuri, Akpojoto; Elmusrati, Mohammed; Välisuo, Petri; Shamsuzzoha, Ahm

doi:10.1109/icl-gnss51451.2021.9452295

Cited by 32 publications

(18 citation statements)

References 42 publications

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“…To facilitate the use of ML techniques in GNSS, it is necessary to systematically review the performance of these ML techniques and their usage from existing literature and studies. To the best of the authors' knowledge, there is no systematic review that focuses on ML techniques utilized in GNSS use cases except for our conference publication presented at the 2021 International Conference on Localization and GNSS (ICL-GNSS) [7]. To achieve this aim, we extensively searched through some relevant digital libraries to identify studies to answer the research questions.…”

Section: The Search Processmentioning

confidence: 99%

“…The publication venues of the selected studies are presented in Table VI From Fig 3, it is interesting to see how the number of publications has been expanding over the years. The types of the selected studies belong to experiment research except for one survey research [7], and one case study research was found [11]. Although most of the selected studies used one form of validation data set to validate ML models, it does not follow that the validation results sufficiently reflect the real situations in the industry.…”

Section: A Overview Of the Final Selected Studiesmentioning

confidence: 99%

“…We perform a systematic review of studies between 2000 and 2021 to analyze and cite examples from the identified literature. This paper is an extension to our conference publication presented at the 2021 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: The Search Processmentioning

confidence: 99%

Section: A Overview Of the Final Selected Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…However, unlike our study, it was based on a statistical approach, and not a machine learning approach. Compared to statistical methods, machine learning methods can identify dependencies in data sets that cannot be modeled mathematically [60]. We classified signal reception conditions by a machine learning method that utilizes five features, whereas the existing method [59] classified the conditions by a statistical method that uses a single metric.…”

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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“…Machine learning has become more important in recent years and is also emerging in the field of GNSS time series analysis, including a wide range of applications [13]. Several studies combine parameters derived from GNSS observations, such as precipitable or integrated water vapor (PVW, IVW) and meteorological data, in a deep learning approach to forecast heavy rainfall [14][15][16] or to perform storm nowcasting [17].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

et al. 2021

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Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.

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Machine Learning Utilization in GNSS—Use Cases, Challenges and Future Applications

Cited by 32 publications

References 42 publications

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

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