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Fingerprinting based WLAN indoor positioning system (FWIPS) provides apromising indoor positioning solution to meet the growing interests for indoorlocation-based services (e.g., indoor way finding or geo-fencing). FWIPS ispreferred because it requires no additional infrastructure for deploying anFWIPS and achieving the position estimation by reusing the available WLAN andmobile devices, and capable of providing absolute position estimation. Forfingerprinting based positioning (FbP), a model is created to provide referencevalues of observable features (e.g., signal strength from access point (AP)) asa function of location during offline stage. One widely applied method to builda complete and an accurate reference database (i.e. radio map (RM)) for FWIPSis carrying out a site survey throughout the region of interest (RoI). Alongthe site survey, the readings of received signal strength (RSS) from allvisible APs at each reference point (RP) are collected. This site survey,however, is time-consuming and labor-intensive, especially in the case that theRoI is large (e.g., an airport or a big mall). This bottleneck hinders the widecommercial applications of FWIPS (e.g., proximity promotions in a shoppingcenter). To diminish the cost of site survey, we propose a probabilistic model,which combines fingerprinting based positioning (FbP) and RM generation basedon stochastic variational Bayesian inference (SVBI). This SVBI based positionand RSS estimation has three properties: i) being able to predict thedistribution of the estimated position and RSS, ii) treating each observationof RSS at each RP as an example to learn for FbP and RM generation instead ofusing the whole RM as an example, and iii) requiring only one time training ofthe SVBI model for both localization and RSS estimation. These benefits make itoutperforms the previous proposed approaches.

Joint Positioning and Radio Map Generation Based on Stochastic Variational Bayesian Inference for FWIPS