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Robust statistical approaches for RSS-based floor detection in indoor localization

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Article number793
Issue number6
Publication statusPublished - 1 Jun 2016
Publication typeA1 Journal article-refereed


Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heardWiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.


  • Floor detection, Indoor localization, Robust regression, RSS-based localization, Trilateration, Weighted centroid localization

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