Gaussian mixture sigma-point particle filter for optical indoor navigation system

Weizhi Zhang, Wenjun Gu, Chunyi Chen, M. I.S. Chowdhury, Mohsen Kavehrad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


With the fast growing and popularization of smart computing devices, there is a rise in demand for accurate and reliable indoor positioning. Recently, systems using visible light communications (VLC) technology have been considered as candidates for indoor positioning applications. A number of researchers have reported that VLC-based positioning systems could achieve position estimation accuracy in the order of centimeter. This paper proposes an Indoors navigation environment, based on visible light communications (VLC) technology. Light-emitting-diodes (LEDs), which are essentially semiconductor devices, can be easily modulated and used as transmitters within the proposed system. Positioning is realized by collecting received-signal-strength (RSS) information on the receiver side, following which least square estimation is performed to obtain the receiver position. To enable tracking of user's trajectory and reduce the effect of wild values in raw measurements, different filters are employed. In this paper, by computer simulations we have shown that Gaussian mixture Sigma-point particle filter (GM-SPPF) outperforms other filters such as basic Kalman filter and sequential importance-resampling particle filter (SIR-PF), at a reasonable computational cost.

Original languageEnglish (US)
Title of host publicationBroadband Access Communication Technologies VIII
StatePublished - 2014
EventPhotonics West 2014 Conference on Broadband Access Communication Technologies VIII - San Francisco, CA, United States
Duration: Feb 4 2014Feb 6 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherPhotonics West 2014 Conference on Broadband Access Communication Technologies VIII
Country/TerritoryUnited States
CitySan Francisco, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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