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Indoor localization algorithm using clustering on signal and coordination pattern

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  • Chen-Yang Cheng

Abstract

In recent years, the demand for indoor location-based services has gradually received greater attention. The presence of multipath interference has a tendency to interfere with traditional algorithms calculated based on received signal strength (RSS). The application of virtual tags can greatly reduce deployment costs and enable greater environmental adaptability. However, an excess of ineffectively filtered virtual tags will only lead to greater error in calculation. Therefore, virtual tags are combined with a two-step clustering method to replace the concept of signal hotpoint intersections due to the mutual interdependence of data in space and the influence of neighboring objects. This study improved a two-step location algorithm that combines the advantages of virtual tags and two-step clustering analysis, called clustering-based localization algorithm, offering significant improvement over most traditional localization algorithms. RSS are no longer used as a basis for clustering, and are replaced by the combination of signal and coordination pattern. Two steps of cluster analysis are performed during the filtering process. The first step utilizes the tags’ signals to perform clustering. The second step incorporates tags’ coordinate for filtering. As the clustering-based localization process considers the interactive relationship between coordinate data, it achieves superior results compared to those produced via methods that only use signal strength to select neighboring solutions. This study then constructs a wireless sensor network and assesses the effectiveness of the algorithm. Copyright Springer Science+Business Media, LLC 2014

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  • Chen-Yang Cheng, 2014. "Indoor localization algorithm using clustering on signal and coordination pattern," Annals of Operations Research, Springer, vol. 216(1), pages 83-99, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:83-99:10.1007/s10479-012-1219-x
    DOI: 10.1007/s10479-012-1219-x
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    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
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