Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/118614
Title: Algorithms for Pervasive Indoor Tracking Systems
Authors: BAO HAITAO
Keywords: step-counting, dead reckoning, map matching, particle filter, sensor fusion, cluster based localization
Issue Date: 31-Mar-2014
Source: BAO HAITAO (2014-03-31). Algorithms for Pervasive Indoor Tracking Systems. ScholarBank@NUS Repository.
Abstract: An extensive amount of research has been conducted on indoor localization. In this thesis, we have made contributions to the design of step-counting dead reckoning (DR) localization systems and the methodologies that can be applied towards a pervasive localization solution. We have improved the location accuracy by the proposed adaptive step direction estimation method. Map matching method and an improved particle filter are proposed to make full use of map information. The results from two firmly attached sensors are fused using maximum likelihood estimation to achieve more robust and accurate results on both orientation estimation and location estimation. In this thesis, a cluster based cooperative localization method is also proposed and evaluated. Three algorithms are implemented and the performance are compared. With the combination of the DR algorithm, further improvements on location availability and accuracy have been achieved.
URI: http://scholarbank.nus.edu.sg/handle/10635/118614
Appears in Collections:Ph.D Theses (Open)

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