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Gaussian processes for RSS fingerprints construction in indoor localization
Ericsson AB, Sverige.ORCID iD: 0000-0003-1214-2391
University of Sheffield, UK.
University of Sheffield, UK.
Ericsson AB, Sverige.
2018 (English)In: 21st International Conference on Information Fusion (FUSION), IEEE, 2018, p. 1377-1384Conference paper, Published paper (Refereed)
Abstract [en]

Location-based applications attract more and more attention in recent years. Examples of such applications include commercial advertisements, social networking software and patient monitoring. The received signal strength (RSS) based location fingerprinting is one of the most popular solutions for indoor localization. However, there is a big challenge in collecting and maintaining a relatively large RSS fingerprint database. In this work, we propose and compare two algorithms namely, the Gaussian process (GP) and Gaussian process with variogram, to estimate and construct the RSS fingerprints with incomplete data. The fingerprint of unknown reference points is estimated based on measurements at a limited number of surrounding locations. To validate the effectiveness of both algorithms, experiments using Bluetooth-low-energy (BLE) infrastructure have been conducted. The constructed RSS fingerprints are compared to the true measurements, and the result is analyzed. Finally, using the constructed fingerprints, the localization performance of a probabilistic fingerprinting method is evaluated.

Place, publisher, year, edition, pages
IEEE, 2018. p. 1377-1384
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-151693DOI: 10.23919/ICIF.2018.8455842ISBN: 978-0-9964527-6-2 (electronic)OAI: oai:DiVA.org:liu-151693DiVA, id: diva2:1252309
Conference
21st International Conference on Information Fusion (FUSION), 10-13 July 2018, Cambridge, UK
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2019-02-12
In thesis
1. Gaussian Processes for Positioning Using Radio Signal Strength Measurements
Open this publication in new window or tab >>Gaussian Processes for Positioning Using Radio Signal Strength Measurements
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Estimation of unknown parameters is considered as one of the major research areas in statistical signal processing. In the most recent decades, approaches in estimation theory have become more and more attractive in practical applications. Examples of such applications may include, but are not limited to, positioning using various measurable radio signals in indoor environments, self-navigation for autonomous cars, image processing, radar tracking and so on. One issue that is usually encountered when solving an estimation problem is to identify a good system model, which may have great impacts on the estimation performance. In this thesis, we are interested in studying estimation problems particularly in inferring the unknown positions from noisy radio signal measurements. In addition, the modeling of the system is studied by investigating the relationship between positions and radio signal strength measurements.

One of the main contributions of this thesis is to propose a novel indoor positioning framework based on proximity measurements, which are obtained by quantizing the received signal strength measurements. Sequential Monte Carlo methods, to be more specific particle filter and smoother, are utilized for estimating unknown positions from proximity measurements. The Cramér-Rao bounds for proximity-based positioning are further derived as a benchmark for the positioning accuracy in this framework.

Secondly, to improve the estimation performance, Bayesian non-parametric modeling, namely Gaussian processes, have been adopted to provide more accurate and flexible models for both dynamic motions and radio signal strength measurements. Then, the Cramér-Rao bounds for Gaussian process based system models are derived and evaluated in an indoor positioning scenario.

In addition, we estimate the positions of stationary devices by comparing the individual signal strength measurements with a pre-constructed fingerprinting database. The positioning accuracy is further compared to the case where a moving device is positioned using a time series of radio signal strength measurements.

Moreover, Gaussian processes have been applied to sports analytics, where trajectory modeling for athletes is studied. The proposed framework can be further utilized to carry out, for instance, performance prediction and analysis, health condition monitoring, etc. Finally, a grey-box modeling is proposed to analyze the forces, particularly in cross-country skiing races, by combining a deterministic kinetic model with Gaussian process.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 51
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1968
Keywords
Gaussian process, positioning, radio signals
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-153944 (URN)10.3384/diss.diva-153944 (DOI)978-91-7685-162-3 (ISBN)
Public defence
2019-03-15, Ada Lovelace, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2019-02-27 Created: 2019-02-12 Last updated: 2019-02-27Bibliographically approved

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Zhao, YuxinGunnarsson, Fredrik

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  • apa
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