Multi-floor indoor location estimation system based on wireless local area network

• Main Author: Chua, Tien Han
• Format: Thesis
• Language: English
• Published: 2007
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/6383/1/ChuaTienHanMFKE2007.pdf; http://eprints.utm.my/id/eprint/6383/
• Institution: Universiti Teknologi Malaysia

The proliferation of high speed wireless technologies and mobile computing infrastructures has fostered a rapid development in location based services. The key to the success of location based services is the estimation of user’s location. Indoor location estimation system using various wireless technologies such as infrared and ultrasound are available. However, these systems require specialized infrastructures and incur high costs. This study focuses on design and development of a software based multi-floor indoor location estimation system using Wireless Local Area Network (WLAN). Location fingerprinting technique is employed to estimate Mobile Terminal’s (MT) location. WLAN Received Signal Strength (RSS) measured by MT is used as location fingerprint. Before location estimation, database of location fingerprint is constructed by collecting histograms of RSS at predefined reference locations. During location estimation, current histogram of RSS at unknown location will be compared to the database. The most probable match is selected and returned as estimated location based on Bayesian filtering algorithm. Estimated location is reported as physical location and symbolic location. Before developing the system, study on characteristics of RSS is conducted to help the design, development and implementation of the proposed system. The proposed system is then designed and developed using Java programming language. The performance of the proposed system is evaluated in a two-floor building using offthe- shelf WLAN access points and client device. Finally, various factors which affect the performance of the proposed system are investigated. From the evaluations in the two-floor building, the proposed system achieved best accuracy of 4.56 meters during stationary tests and 4.54 meters during mobile tests with 90% precision. The best percentage of correct floor estimation is 100% for both tests.