Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks

Sensors

Philipp Richter¹  Manuel Toledano-Ayala² 

[1] Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n., Col. Las Campanas, Santiago de Querétaro 76010, Mexico; E-Mail

关键词: sensor modeling;    WLAN received signal strength;    Gaussian process regression;    machine learning;    location fingerprinting;   

DOI  :  10.3390/s150922587

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.