The commercial potential of indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Fingerprinting has attracted much attention recently due to its adaptivity to none-line-of-sight measurement from access points (APs) and high applicability in complex indoor environment.

Offering quality ILBS requires accurate indoor positioning. In this thesis, we study several approaches to make Wi-Fi fingerprinting highly accurate. The approaches are to mitigate noisy signal measurement, to fuse distance sensor with fingerprinting, and to adaptively learn fingerprint patterns over time. We will conduct extensive experimental studies to validate the performance of the approaches.

Previous fingerprinting positioning based on certain simila...[ Read more ]

The commercial potential of indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Fingerprinting has attracted much attention recently due to its adaptivity to none-line-of-sight measurement from access points (APs) and high applicability in complex indoor environment.

Offering quality ILBS requires accurate indoor positioning. In this thesis, we study several approaches to make Wi-Fi fingerprinting highly accurate. The approaches are to mitigate noisy signal measurement, to fuse distance sensor with fingerprinting, and to adaptively learn fingerprint patterns over time. We will conduct extensive experimental studies to validate the performance of the approaches.

Previous fingerprinting positioning based on certain similarity metric often suffers from ambiguous matching problem of reference points, resulting in high decision uncertainty. To address this, we propose a novel approach based on junction of signal tiles, which are formed based on the first two moments of the signals. The target location is then constrained within the junction area. This overcomes position ambiguity problem and achieves highly accurate positioning.

To further enhance the localization accuracy, we study how to fuse fingerprint with distance information. Our approach is applicable to a wide range of sensors (peer-assisted, inertial navigation sensor, beacons, etc.) and wireless fingerprints (Wi-Fi, Bluetooth, etc.). By a novel optimization formulation which jointly fuses distance bounds and measured fingerprint signals, it achieves low positioning errors even under complex indoor environment.

Fingerprinting accuracy deteriorates if the AP signals are altered (due to AP movement, partitioning, etc.). To address this, the signal map needs to be adapted overtime. We propose and study a novel clustering-based scheme which can localize targets despite AP alteration, and can identify the altered APs. Using a novel online learning approach, our algorithm can also adapt the fingerprint map to the altered signal environment.