Wireless Positioning In Indoor Environment

The communication technology has explosively grown in last decade, and the rise of the Internet changed the life style of all people in theworld. Nowadays, wireless communication is essential to our daily life. People can retrieve information from the Internet though a mobile device, and we are stepping to the age of ubiquitous computing. In order to provide proper information to users according to their location, we need a positioning system, which can estimate the location of a user. Although Global Positioning System (GPS) can provide the location of a user with good precision, it doesn’t work when the user stays in an indoor environment. However, the IEEE802.11wireless network has become popular in past few years, and most of the urban areas are covered by wireless LAN (WLAN) now,especially the indoor environment, such as commercial buildings, government buildingsand shopping mall. Therefore, a WLAN positioning system can be a solution to provide location-based services in the indoor environment.In this research, we propose a new positioning algorithm, which can have higher accuracy in challenging environment, such as Equipment room, which has crowned metal cabinets. While most of the existing WLAN positioning systems obtain Received Signal Strength (RSS) information at either the access points or at the mobile device, in this research, we propose a new WLAN positioning approach by making use of the RSS collected at both the access points and mobile device and hence, achieving better precision. We have designed a new WLAN positioning model based on asymmetrical signal. This positioning model can fully utilize the RSS information in between the mobile device and access points. Most of the previous works on WLAN positioning measure signal strength at either access point or mobile device. In this model, we use both RSS, i.e. measure the signal at both access point and mobile device, to enhance the performance of positioning. Based on our new positioning model, we have studied the implementation of the model with different ways to combine the uplink data (i.e. signal transmitted from mobile device to access point) and downlink data (i.e.signal transmitted from access point to mobile device), and evaluate the performance of these implementations. Moreover, the proposing positioning model is algorithm independent, which means that it can be applied with any RSS-based positioning algorithm. Based on the proposing approach, we developed a WLAN positioning system, which provide two different techniques, Composed Distance and Probability, to utilize the RSS data to enhance the positioning performance.