Using Machine Learning to Improve Accuracy and Robustness of Indoor Positioning under Practical Usage Scenarios

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Using Machine Learning to Improve Accuracy and Robustness of Indoor Positioning under Practical Usage Scenarios
Title:
Using Machine Learning to Improve Accuracy and Robustness of Indoor Positioning under Practical Usage Scenarios
Journal Title:
2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Keywords:
Publication Date:
10 January 2023
Citation:
Mendoza Santos, R. X., & Krishnan, S. (2022). Using Machine Learning to Improve Accuracy and Robustness of Indoor Positioning under Practical Usage Scenarios. 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). https://doi.org/10.1109/icarcv57592.2022.10004281
Abstract:
Indoor Positioning Systems (IPSs) can increase productivity in both office and industrial settings. They continue to become more accurate and robust as the advent of machine learning enables them to overcome the limitations of traditional positioning techniques. Despite this, the mainstream incorporation of IPS is currently hindered by significant infrastructure cost, especially for areas that cannot attain sufficient wireless coverage due to budget or environmental constraints. This paper therefore explores the use of machine learning for infrastructure-limited smartphone-based localization while adhering to practical constraints. The performance of the trained models was compared to that of conventional multilateration while also considering the effect of phone placement on positioning accuracy. Experimental results showed that the model trained under harsher conditions proved to be the most robust for both handheld and pocket mobile tests.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-6654-7687-4
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