RSS-based Indoor Localization Using Belief Function Theory

A. Achroufène 1, 2 Yacine Amirat 1 A. Chibani 1
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : Received Signal Strength (RSS) is a simple and lowcost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor wireless sensor network environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster-Shafer theory (DST) to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness and computation time and its superiority compared with state-of-the-art methods.
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Submitted on : Friday, September 21, 2018 - 3:17:37 PM
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A. Achroufène, Yacine Amirat, A. Chibani. RSS-based Indoor Localization Using Belief Function Theory. IEEE Transactions on Automation Science and Engineering, Institute of Electrical and Electronics Engineers, 2018, pp.1-18. ⟨10.1109/TASE.2018.2873800⟩. ⟨hal-01878843⟩



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