An automatic approach to fingerprint construction of indoor localization by crowd paths

In typical indoor position system employing location fingerprints model, received signal strength indications (RSSI) from a set of Wi-Fi access points are used as an unique fingerprint to identify a specific position. This type position systems need abundant Wi-Fi fingerprints, generally implemented by trained experts, which extends labor costs and restricts heir promotion. In his paper, a novel approach to construct intelligently Wi-Fi fingerprint database based on crowd paths triggered by lots of ordinary users holding onto smartphone is proposed. As existing drift errors in inertial measurement unit (IMU) and Wi-Fi module and the knowledge that crowd paths involve massive similar or crossing positions, we defined a concept: thin landmarks, and employ a fuzzy voter scheme to locating each thin landmark. Then rectifying the position of each sample point in every thin landmark´s candidate set with corresponding thin landmark´s coordinate and simultaneously utilizing particle filter (PF) algorithm to smooth each sample point on each crowd path. We implemented the approach on off-he-shelf smartphones and evaluate he performance in our campus. Experimental result indicates that the approach can availably construct Wi-Fi fingerprint database in tolerable errors.