Accurate WiFi localization by unsupervised fusion of extended candidate location set

Fusing the predictions of multiple received signal strength (RSS)-based classifiers is an efficient strategy to mitigate the impact of the fluctuation of the RSS. However, most of the existing fusion methods exhibit two remarkable shortcomings: 1) they need to train and store offline weights by the supervised learning and 2) they directly fuse the so-called candidate location set (CLS), which is collected from the most likely location estimate of each classifier (location with the largest probability of being the true location predicted by the classifier), and thus do not fully leverage the knowledge of classifiers. In general, the fluctuation of RSS does not guarantee the location predicted by each classifier with the highest probability to be the true location, thus leading to severe performance degeneration of the existing fusion methods. To overcome the above shortcomings, we propose an accurate WiFi localization framework by unsupervised fusion of an extended CLS (ECLS). First, we train multiple classifiers by only using RSS fingerprints in the offline phase. In the online phase, instead of collecting the CLS from the trained classifiers, we construct an ECLS by augmenting CLS with other location estimates (locations with predication probability greater than a certain threshold) from each classifier. As compared with the CLS, ECLS provides a bigger fusion space that likely includes the true location of the user. Furthermore, an unsupervised fusion localization algorithm based on the ECLS is derived from the joint optimization of weights and the location of the user. Furthermore, a point of inflection searching algorithm is also proposed to intelligently construct the ECLS. Real experimental results show that our proposed algorithm is more robust to changing environments and model errors, and can significantly improve the localization accuracy without any fingerprint and hardware calibrations.