Improving Precision GNSS Positioning and Navigation Accuracy on Smartphones using Machine Learning

Siemuri, Akpojoto; Selvan, Kannan; Välisuo, Petri; Elmusrati, Mohammed

doi:10.33012/2021.18004

Cited by 6 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have presented the opinion that combining two or more ML techniques may potentially enhance the power of the estimation model. This can hold true in the field of GNSS as shown in [77].…”

Section: E ML Vs Non-ml Models (Rq4a)mentioning

confidence: 84%

“…ML models have been applied to other positioning and navigation applications such as regional mapping of the geoid [73]; human mobility analysis on large-scale mobility data which has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health [74]. Other applications include location prediction using GPS trackers to, for example, locate missing people with dementia [75]; improving GNSS Positioning from smartphones [76][77][78][79]; improving GPS code phase positioning accuracy in urban environments [80]; improving accuracy of differential GPS (DGPS) correction prediction in position domain [81]; and improving kinematic GNSS positioning accuracy with lowcost GNSS receiver in urban environments [19]. Another study used the combination of Genetic Algorithms (GA) and neural networks for exploring the navigation satellite constellation design tradespace to speed up the constellation performance computation and for an improved GNSS integrity [82].…”

Section: Gnss Navigation and Precise Positioningmentioning

confidence: 99%

“…In the case of the implications for practitioners, this review has found that very few of the selected studies focus on industry practice, for example, [76][77][78]. This may be evidence that the application of ML models in the real world of the GNSS industry is still quite limited.…”

Section: Implications For Research and Practicementioning

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.

View full text Add to dashboard Cite

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

show abstract

Section: E ML Vs Non-ml Models (Rq4a)mentioning

confidence: 84%

Section: Gnss Navigation and Precise Positioningmentioning

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.

View full text Add to dashboard Cite

show abstract

“…Instead of learning the position directly, one can instead learn the positioning correction, which refers to the offset of the baseline position from a standard algorithm such as the weighted least-squares (WLS) or Kalman filter algorithm from the ground truth. In the work by Siemuri et al (2021), the authors trained machine learning algorithms such as linear regression, Bayesian ridge regression, and neural network algorithms as well as a weighted combination of all three approaches to predict the positioning correction. The results showed that the weighted combination approach outperformed all three algorithms in terms of positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

Learning GNSS Positioning Corrections for Smartphones Using Graph Convolution Neural Networks

Mohanty,

Gao

2023

navi

View full text Add to dashboard Cite

Smartphone receivers comprise approximately 1.5 billion global navigation satellite system receivers currently manufactured worldwide. Smartphone receivers provide measurements with lower signal levels and higher noise than commercial receivers. Because of constraints on size, weight, power consumption, and cost, it is challenging to achieve accurate positioning with these receivers, particularly in urban environments. Traditionally, global positioning system measurements are processed via model-based approaches, such as weighted least-squares and Kalman filtering approaches. While model-based approaches can provide meter-level positioning accuracy in a postprocessing manner, these approaches require strong assumptions on the corresponding noise models and require manual tuning of parameters such as covariances. In contrast, learning-based approaches have been proposed that make fewer assumptions about the data structure and can accurately model environment-specific errors. However, these approaches provide lower accuracy than model-based methods and are sensitive to initialization. In this paper, we propose a hybrid framework for learning position correction, which corresponds to the offset between the true receiver position and the estimated position. For a learning-based approach, we propose a graph convolution neural network (GCNN) that can learn different graph structures with multi-constellation and multi-frequency signals. For better initialization of the GCNN, we use a Kalman filter to estimate a coarse receiver position.We then use this coarse receiver position to condition the input features to the graph. We test our proposed approach on real-world data sets from the Google Smartphone Decimeter Challenge and show improved positioning performance over model-based methods such as the weighted least-squares and Kalman filter methods.

show abstract