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Positioning Based on Noise-Limited Censored Path Loss Data

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Standard

Positioning Based on Noise-Limited Censored Path Loss Data. / Karttunen, Aki; Valkama, Mikko; Talvitie, Jukka.

2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings. ed. / Jari Nurmi; Elena-Simona Lohan; Joaquin Torres-Sospedra; Heidi Kuusniemi; Aleksandr Ometov. IEEE, 2020. (2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Karttunen, A, Valkama, M & Talvitie, J 2020, Positioning Based on Noise-Limited Censored Path Loss Data. in J Nurmi, E-S Lohan, J Torres-Sospedra, H Kuusniemi & A Ometov (eds), 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings. 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings, IEEE, International Conference on Localization and GNSS, 2/06/20. https://doi.org/10.1109/ICL-GNSS49876.2020.9115572

APA

Karttunen, A., Valkama, M., & Talvitie, J. (2020). Positioning Based on Noise-Limited Censored Path Loss Data. In J. Nurmi, E-S. Lohan, J. Torres-Sospedra, H. Kuusniemi, & A. Ometov (Eds.), 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings (2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings). IEEE. https://doi.org/10.1109/ICL-GNSS49876.2020.9115572

Vancouver

Karttunen A, Valkama M, Talvitie J. Positioning Based on Noise-Limited Censored Path Loss Data. In Nurmi J, Lohan E-S, Torres-Sospedra J, Kuusniemi H, Ometov A, editors, 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings. IEEE. 2020. (2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings). https://doi.org/10.1109/ICL-GNSS49876.2020.9115572

Author

Karttunen, Aki ; Valkama, Mikko ; Talvitie, Jukka. / Positioning Based on Noise-Limited Censored Path Loss Data. 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings. editor / Jari Nurmi ; Elena-Simona Lohan ; Joaquin Torres-Sospedra ; Heidi Kuusniemi ; Aleksandr Ometov. IEEE, 2020. (2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings).

Bibtex - Download

@inproceedings{c46ec001fe0344609b01fa7f2350db2e,
title = "Positioning Based on Noise-Limited Censored Path Loss Data",
abstract = "Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30{\%}) in positioning accuracy.",
keywords = "censored data, localization, maximum-likelihood estimation, path loss, path loss model, positioning, probabilistic modeling., shadow fading, wireless networks",
author = "Aki Karttunen and Mikko Valkama and Jukka Talvitie",
year = "2020",
month = "6",
doi = "10.1109/ICL-GNSS49876.2020.9115572",
language = "English",
series = "2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings",
publisher = "IEEE",
editor = "Jari Nurmi and Elena-Simona Lohan and Joaquin Torres-Sospedra and Heidi Kuusniemi and Aleksandr Ometov",
booktitle = "2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Positioning Based on Noise-Limited Censored Path Loss Data

AU - Karttunen, Aki

AU - Valkama, Mikko

AU - Talvitie, Jukka

PY - 2020/6

Y1 - 2020/6

N2 - Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30%) in positioning accuracy.

AB - Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30%) in positioning accuracy.

KW - censored data

KW - localization

KW - maximum-likelihood estimation

KW - path loss

KW - path loss model

KW - positioning

KW - probabilistic modeling.

KW - shadow fading

KW - wireless networks

U2 - 10.1109/ICL-GNSS49876.2020.9115572

DO - 10.1109/ICL-GNSS49876.2020.9115572

M3 - Conference contribution

T3 - 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings

BT - 2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings

A2 - Nurmi, Jari

A2 - Lohan, Elena-Simona

A2 - Torres-Sospedra, Joaquin

A2 - Kuusniemi, Heidi

A2 - Ometov, Aleksandr

PB - IEEE

ER -