Machine Learning Based NLOS Radio Positioning in Beamforming Networks

Klus, Roman; Talvitie, Jukka; Vinogradova, Julia; Torsner, Johan; Valkama, Mikko

doi:10.1109/spawc51304.2022.9834010

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Digital Object Identifier 10.1109/JISPIN.2023.3275118 addition, 5G networks can provide accurate position estimation in urban areas where the base stations and line-of-sight (LOS) signals are available [3], [4]. Instead, for indoor environments, where the anchors of radio frequency (RF) signals such as WiFi or ultrawideband (UWB) are available, multilateration based on range measurements can provide a good positioning solution [5].…”

Section: Introductionmentioning

confidence: 99%

Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks

Kia

David

Herbruggen

et al. 2023

J. Ind. Sea. Pos. Nav.

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Section: Introductionmentioning

confidence: 99%

Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks

Kia

David

Herbruggen

et al. 2023

J. Ind. Sea. Pos. Nav.

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“…Furthermore, researchers use the k-NN algorithm as a core positioning system while evaluating other features of the positioning pipeline, such as data augmentation to enrich the radio map [10], [11] or to reconstruct areas with missing reference points [12]. Finally, it is the de-facto baseline for fingerprint-based positioning [13]- [17].…”

Section: Introductionmentioning

confidence: 99%

Let’s Talk about k-NN for Indoor Positioning: Myths and Facts in RF-based Fingerprinting

Torres-Sospedra,

Pendão,

Silva

et al. 2023

2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.

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“…[74], as specified within the current 3GPP standardization [75], [76]. RSS-based positioning offers straight-forward data acquisition, does not require the network nodes and UEs to distinguish the signal's direction of arrival, neither requires the timing synchronization [77], [78].…”

Section: Author's Contributionsmentioning

confidence: 99%

“…movement and fall detection [87], as well as supporting the radio resource management from the network point of view [19], [77]. As the considered k-NN fingerprinting positioning system operates within the device, storing and processing the voluminous datasets of measurements to estimate the location requires additional power and resource consumption on devices with limited battery, storage, and performance capabilities.…”

Section: Dimensionality Reduction Techniques For Effortless Indoor Po...mentioning

confidence: 99%

From Compression of Wearable-based Data to Effortless Indoor Positoning

Klus

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This thesis has been completed in a joint/double Doctoral Degree program between TAU and UJI as a part of the A-WEAR project for which I gratefully acknowledge the funding we have received from the European Union's Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreement No. 813278, A-WEAR. Without this support, this work would have never been possible.Furthermore, I would like to express my deepest gratitude to everyone, who has helped me to get to this point, supported me, and made me grow, both professionally and personally.First and foremost, I would like to express my sincere gratitude to my thesis supervisors. Thank you, Prof. Jari Nurmi, for letting me learn from the best, for your unlimited support, and encouragement throughout my ventures as a doctoral student. Thank you, Prof. Elena Simona Lohan, for the academic guidance, for teaching me how to grow as a researcher, as well as for all the care and encouragement you gave me throughout the years. Thank you, Dr. Carlos Granell Canut, xv for everything you did for me, your leadership, and your support. I would also like to extend my gratitude to Dr. Joaquín Torres-Sospedra (Ximo) for acting as my unofficial supervisor, for always supporting me in realizing my ideas, giving me invaluable feedback on my work, and helping me to see the paths ahead. Your guidance is highly appreciated. Furthermore, I would like to acknowledge and thank Dr. Jukka Talvitie for his support, fruitful discussions, and for letting me know about the existence of the A-WEAR project. Without you, I would have never been here. I would like to extend my thanks to Prof. Mikko Valkama, who provided me with motivation and guidance, and to my friend Asst. Prof. Bo Tan for continuous support, encouragement, and inspiration.I would like to extend my recognition to my thesis pre-examiners Prof. Fernando Javier Álvarez Franco and Dr. Christos Laoudias, as well as to my opponents Dr.Tobias Feigl, and Dr. Christos Laoudias, for investing time, energy, and effort into evaluating my thesis and offering me invaluable feedback. I am honored that scientists of such magnitude read my work.I would like to thank my colleagues and co-authors, with whom I had the pleasure to collaborate and learn from over the years. I would like to extend my thanks to all the staff of A-WEAR, TUNI, and UJI, who enabled me to successfully progress through my doctoral studies. I thank all other ESRs for creating a friendly and productive environment across the universities and for always supporting each other.Especially, I would like to thank Darwin for the fruitful collaboration that grew into a friendship. I would also like to extend my gratitude to Alex who has helped me, guided me, and always spoken his mind. To Olga and Nadia, for mutual support and for the memorable time spent together, to my friend Tomáš, as well as to Salwa, Asad, Pável, Viktoriia, and many others.Furthermore, I would like to acknowledge my colleagues and friends, with whom I have spent countless good moments he...

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Machine Learning Based NLOS Radio Positioning in Beamforming Networks

Cited by 4 publications

References 10 publications

Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks

Toward Seamless Localization: Situational Awareness Using UWB Wearable Systems and Convolutional Neural Networks

Let’s Talk about k-NN for Indoor Positioning: Myths and Facts in RF-based Fingerprinting

From Compression of Wearable-based Data to Effortless Indoor Positoning

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