Design and Implementation of WiFi Indoor Localization based on Gaussian Mixture Model and Particle Filter

Up to now Scene Analysis has been based on the WiFi location estimation technique and it has been necessary to have a large scale database and a large amount of calculation. We propose a WiFi estimation method that uses little data or calculation. First of all we apply Gaussian Mixture Model to represent the large scale WiFi database to decrease the WiFi data by no less than 95%. Secondly, we apply Particle Filter to adjust the possible calculation quantity needed for the location estimation technique. As experimental result, we achieved real-time location estimation within 6~10m. Another important issue for Scene Analysis technique is the high cost of operation of the previous WiFi observation. Accordingly crowdsourcing approach was used, employing as system where some users could contribute and other uses could share. The ideal system is a composition of the Web and mobile terminal. WiFi data observed by mobile terminals is uploaded to a Web server where it is managed and integrated into GMM and large scale operations are carried out on data and calculations. When the lightweight modeled data is downloaded to the mobile-terminal, the mobile terminal then has the ability to carry out real-time location estimation independently.