A low-complexity data-fusion algorithm based on adaptive weighting for location estimation

Yih Shyh Chiou, Fuan Tsai, Sheng Cheng Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this paper, a tracking scheme based on adaptive weighted technique is proposed to reduce the computational load of traditional data-fusion algorithm for heterogeneous measurements. For the location-estimation technique with the data fusion of radio-based ranging measurement and speed-based sensing measurement, the proposed tracking scheme based on the Bayesian approach is handled by a state space model; a weighted technique with the reliability of message passing is based on the error propagation law. As compared with a traditional data-fusion algorithm based on a Kalman filtering approach, the proposed scheme that combines radio ranging measurement with speed sensing measurement for data fusion has much lower computational complexity with acceptable location accuracy.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Information Security and Intelligent Control, ISIC 2012
Pages294-297
Number of pages4
DOIs
StatePublished - 2012
Event3rd International Conference on Information Security and Intelligent Control, ISIC 2012 - Yunlin, Taiwan
Duration: 14 Aug 201216 Aug 2012

Publication series

NameProceedings - 3rd International Conference on Information Security and Intelligent Control, ISIC 2012

Conference

Conference3rd International Conference on Information Security and Intelligent Control, ISIC 2012
Country/TerritoryTaiwan
CityYunlin
Period14/08/1216/08/12

Keywords

  • Bayesian approach
  • data-fusion
  • error propagation
  • Kalman filtering
  • location estimation
  • tracking

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