| Summary: | Taking advantage of widely deployed access points (AP), WiFi fingerprint based localization is of importance in indoor internet-of-things (IOT) environments. Nevertheless, spatio-temporal variation is one of its intractable problems, indicating severely environmental dynamics and uncertainty of decision. In this case, the localization accuracy drops significantly. In this paper, we attempt to overcome effects of spatio-temporal variations from two aspects: filtering of the training data and selection of partially valuable APs for matching in test phase. The key idea is to match partial unaffected measurements with `clean' unaffected fingerprints. Bayesian framework and category model are presented for WiFi fingerprints. Two binary hidden variables with different dimensions are introduced to identify singular fingerprints and affected measurements respectively by employing expectation-maximization (EM) algorithms. EM based filter and simultaneous AP selection and localization are then proposed to obtain an optimal matching. Experimental results show that our proposed scheme greatly improves the localization accuracy in severely dynamic indoor environments.
|
|---|
