TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Positioning Based on Noise-Limited Censored Path Loss Data

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings
ToimittajatJari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)9781728164557
DOI - pysyväislinkit
TilaJulkaistu - kesäkuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Localization and GNSS -
Kesto: 2 kesäkuuta 20204 kesäkuuta 2020

Julkaisusarja

Nimi2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings

Conference

ConferenceInternational Conference on Localization and GNSS
Ajanjakso2/06/204/06/20

Tiivistelmä

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.