Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning
Ericsson Research, Linköping, Sweden.
Ericsson Research, Linköping, Sweden.
Ericsson Research, Linköping, Sweden.
Ericsson Research, Linköping, Sweden.
Show others and affiliations
2016 (English)In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), IEEE , 2016, 1-5 p.Conference paper, Published paper (Refereed)
Abstract [en]

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

Place, publisher, year, edition, pages
IEEE , 2016. 1-5 p.
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-128255DOI: 10.1109/VTCSpring.2016.7504255ISI: 000386528400206ISBN: 9781509016983 (print)OAI: oai:DiVA.org:liu-128255DiVA: diva2:930410
Conference
2016 IEEE 83rd Vehicular Technology Conference: VTC2016-Spring, 15–18 May 2016, Nanjing, China
Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2017-09-13Bibliographically approved
In thesis
1. Position Estimation in Uncertain Radio Environments and Trajectory Learning
Open this publication in new window or tab >>Position Estimation in Uncertain Radio Environments and Trajectory Learning
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To infer the hidden states from the noisy observations and make predictions based on a set of input states and output observations are two challenging problems in many research areas. Examples of applications many include position estimation from various measurable radio signals in indoor environments, self-navigation for autonomous cars, modeling and predicting of the traffic flows, and flow pattern analysis for crowds of people. In this thesis, we mainly use the Bayesian inference framework for position estimation in an indoor environment, where the radio propagation is uncertain. In Bayesian inference framework, it is usually hard to get analytical solutions. In such cases, we resort to Monte Carlo methods to solve the problem numerically. In addition, we apply Bayesian nonparametric modeling for trajectory learning in sport analytics.

The main contribution of this thesis is to propose sequential Monte Carlo methods, namely particle filtering and smoothing, for a novel indoor positioning framework based on proximity reports. The experiment results have been further compared with theoretical bounds derived for this proximity based positioning system. To improve the performance, Bayesian non-parametric modeling, namely Gaussian process, has been applied to better indicate the radio propagation conditions. Then, the position estimates obtained sequentially using filtering and smoothing are further compared with a static solution, which is known as fingerprinting.

Moreover, we propose a trajectory learning framework for flow estimation in sport analytics based on Gaussian processes. To mitigate the computation deficiency of Gaussian process, a grid-based on-line algorithm has been adopted for real-time applications. The resulting trajectory modeling for individual athlete can be used for many purposes, such as performance prediction and analysis, health condition monitoring, etc. Furthermore, we aim at modeling the flow of groups of athletes, which could be potentially used for flow pattern recognition, strategy planning, etc.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 45 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1772
National Category
Control Engineering Signal Processing Probability Theory and Statistics Computer Vision and Robotics (Autonomous Systems) Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-135425 (URN)10.3384/lic.diva-135425 (DOI)9789176855591 (ISBN)
Presentation
2017-03-29, Visionen, Hus B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2017-03-14 Created: 2017-03-14 Last updated: 2017-03-14Bibliographically approved

Open Access in DiVA

fulltext(534 kB)127 downloads
File information
File name FULLTEXT02.pdfFile size 534 kBChecksum SHA-512
f651bed9709cbbe9c5c1deaf5fb4a19f505141858a478a1efe5ae9cdb7c702678b0708bccc8b803c4f018649f7decb4efed0b1a871b0e600e331cd73de1fe16a
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Zhao, YuxinGunnarsson, FredrikAmirijoo, MehdiHendeby, Gustaf
By organisation
Automatic ControlFaculty of Science & Engineering
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 127 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 335 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf