Taming the inconsistency of Wi-Fi fingerprints for device-free passive indoor localization

Device-free Passive (DfP) indoor localization releases the users from the burden of wearing sensors or carrying smartphones. Instead of locating devices, DfP technology directly locates human bodies. This promising technology upgrades and even redefines many services, such as intruder alarm, fire rescue, fall detection, baby monitoring, etc. Using Wi-Fi based fingerprints, DfP approaches can achieve a nearly perfect accuracy with a resolution less than one meter. However, Wi-Fi localization profiles may easily drift with a minor environment change, resulting in an inconsistency between fingerprints and new profiles. This inconsistency issue could lead to large errors, and may quickly ruin the whole system. To address this issue, we propose a approach named AutoFi to automatically calibrate the localization profiles in an unsupervised manner. AutoFi embraces a new technique that online estimates and cancels profile contaminants introduced by environment changes. It applies an autoencoder to preserve critical features of fingerprints, and reproduces them later in new localization profiles. Experiment results demonstrate that AutoFi indeed rescues the Wi-Fi fingerprints from variations in the surrounding. The localization accuracy is improved from 18.8% (before auto-calibration) to 84.9% (after auto-calibration).