Event Details
Received Signal Strength Calibration for Wireless Local Area Network (WLAN) Localization
Presenter: Diego Felix, M.A.Sc. Candidate
Supervisor:
Date: Mon, July 19, 2010
Time: 09:00:00 - 10:00:00
Place: EOW 430
ABSTRACT
ABSTRACT
Terminal localization for indoor Wireless Local Area Networks (WLAN) is critical for the deployment of location-aware computing inside of buildings. Three major challenges for indoor location aware computing are addressed in this seminar: first, to decrease the influence of outliers introduced in the distance measurements by Non-Line-of-Sight (NLoS) propagation when an ultrasonic sensor network is used for data collection; second, to obtain high localization accuracy in the presence of fluctuations in the Received Signal Strength (RSS) measurements caused by multipath fading; and third, to determine an automated calibration method to reduce large variations in RSS levels when different mobile devices need to be located. A robust window function is developed to mitigate the influence of outliers in survey terminal localization. Furthermore, spatial filtering of the RSS signals to reduce the effect of the distance-varying portion of noise is proposed. Two different survey point geometries are tested with the noise reduction technique: survey points arranged in sets of tight clusters and survey points uniformly distributed over the network area.
Finally, an affine transformation is introduced as RSS calibration method between mobile devices and an automated calibration procedure based on the Expectation-Maximization (EM) algorithm is developed.
The results show that the survey terminal localization mean distance error with our robust method is 7.36 cm. In addition, when the spatial averaging noise reduction filter is used the location accuracy improves by 16% and by 18% when the filter is applied to a clustered survey set as opposed to a straight-line survey set. Lastly, the location accuracy is within 2m when an affine function is used for RSS calibration and the automated calibration algorithm converged to the optimal transformation parameters after it was iterated for 11 locations.