liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning
Research, Ericsson AB, 39174 Stockholm, Sweden.ORCID iD: 0000-0003-1214-2391
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
SSE, Chinese University of Hong Kong Shenzhen, Shenzhen, China.
Ericsson Research, Linköping, Sweden.
Show others and affiliations
2018 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, no 6, p. 5372-5386Article in journal (Refereed) Published
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 positioning of devices based on a time series of proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Motion model will be needed together with the measurements to determine the position of the device. Therefore, sequential Monte Carlo methods, namely particle filtering and smoothing, are applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report, and is further compared to parametric Cramér-Rao lower bounds. Finally, the position accuracy is also evaluated with real experimental data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 67, no 6, p. 5372-5386
Keywords [en]
Proximity, indoor positioning, particle filtering and smoothing, Cramer-Rao lower bounds
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-147834DOI: 10.1109/TVT.2018.2799174ISI: 000435553400053Scopus ID: 2-s2.0-85041415767OAI: oai:DiVA.org:liu-147834DiVA, id: diva2:1205778
Note

Funding agencies: European Union FP7 Marie Curie Training Programme on Tracking in Complex Sensor Systems (TRAX) [607400]; NSFC [61701426]; Shenzhen Science and Technology Innovation Council [JCYJ20170307155957688, JCYJ20170411102101881]

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2019-02-12Bibliographically approved
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

Open Access in DiVA

fulltext(1185 kB)102 downloads
File information
File name FULLTEXT02.pdfFile size 1185 kBChecksum SHA-512
5d1902c3daaa032a1d3c176372b008f7626cc38b177af9a795142686cb5aef97737ccbe9b66ee1d4ad78230baabb4e8094acc6f14272815a5318e2347444c863
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Zhao, YuxinFritsche, CarstenGunnarsson, FredrikGustafsson, Fredrik

Search in DiVA

By author/editor
Zhao, YuxinFritsche, CarstenGunnarsson, FredrikGustafsson, Fredrik
By organisation
Automatic ControlFaculty of Science & Engineering
In the same journal
IEEE Transactions on Vehicular Technology
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 102 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
urn-nbn

Altmetric score

doi
urn-nbn
Total: 248 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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