Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks

Philipp Richter, Manuel Toledano-Ayala

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)
112 Downloads (Pure)


Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.
Original languageEnglish
Pages (from-to)22587-22615
Issue number9
Publication statusPublished - 15 Aug 2015
Externally publishedYes
Publication typeA1 Journal article-refereed


  • sensor modeling
  • Gaussian process inference
  • Machine learning
  • fingerprinting


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