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Abstract
Location information is increasingly valuable in mobile services. While it is a wide consensus that global navigation satellite systems (GNSS) like Global Positioning System (GPS) dominate outdoor localization, there is no ubiquitous and straightforward solution for indoor positioning. However, with the popularization of Wi-Fi access points (APs) in public areas, received signal strength (RSS) based approaches show their advantages. Among them, the fingerprinting approach seems to have the most potential. It can provide very accurate service while it exploits rather than suffer from non-line-of-sight (NLOS) propagation.
However, the fingerprinting approach has its own drawbacks. It requires a training phase to establish and maintain a database and its training phase could be very time-consuming because it requires the surveyor to measure or record the location of each reference point (RP) manually at each time they survey the target environment. This thesis mainly focuses on optimizing the fingerprinting approach by adopting different positioning technologies. IMU and RFID devices are applied in training phase of the fingerprinting approach. The experiments using this method are based on self-designed hardware. The location fingerprint can be collected in an automatic way with positioning error less than 3m.
The main findings of this thesis are list below:
A novel method of generating and maintaining a fingerprint database for fingerprint-based indoor positioning systems.
A self-designed hardware solution for fingerprint data collection and the related programs for device control and data post processing.
A new approach for step length measurement and walking status detection for waist mounted IMU equipment.