Analysis of the Accuracy of a Magnetic Field-Based Positioning System Including the Environment of a Parking Vehicle

Scholle, Oscar J. M.; Lomonova, E.A.; Vrijsen, N. H.

doi:10.1109/ever48776.2020.9243121

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Evaluation of an Optical Wireless Positioning System in a Parking Garage

De Bruycker,

Raes,

Altekar

et al. 2024

2024 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Evaluation of an Optical Wireless Positioning System in a Parking Garage

De Bruycker,

Raes,

Altekar

et al. 2024

2024 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Machine-Learning Approach for Solving Inverse Problems in Magnetic-Field-Based Positioning

Sasaki

FUKUSHIMA²

2022

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

Magnetic fields are often utilized for position sensing of mobile devices. In typical sensing systems, multiple sensors are used to detect magnetic fields generated by target devices. To determine the positions of the devices, magnetic-field data detected by the sensors must be converted to device-position data. The data conversion is not trivial because it is a nonlinear inverse problem. In this study, we propose a machinelearning approach suitable for data conversion required in the magneticfield-based position sensing of target devices. In our approach, two different sets of training data are used. One of the training datasets is composed of raw data of magnetic fields to be detected by sensors. The other set is composed of logarithmically represented data of the fields. We can obtain two different predictor functions by learning with these training datasets. Results show that the prediction accuracy of the target position improves when the two different predictor functions are used. Based on our simulation, the error of the target position estimated with the predictor functions is within 10 cm in a 2 m × 2 m × 2 m cubic space for 87% of all the cases of the target device states. The computational time required for predicting the positions of the target device is 4 ms. As the prediction method is accurate and rapid, it can be utilized for the real-time tracking of moving objects and people.

show abstract

Analysis of the Accuracy of a Magnetic Field-Based Positioning System Including the Environment of a Parking Vehicle

Cited by 2 publications

References 27 publications

Evaluation of an Optical Wireless Positioning System in a Parking Garage

Evaluation of an Optical Wireless Positioning System in a Parking Garage

Machine-Learning Approach for Solving Inverse Problems in Magnetic-Field-Based Positioning

Contact Info

Product

Resources

About