GPS/Wi-Fi/ MEMS Sensors Integration for Indoor Navigation

GPS, MEMS sensors and Wi-Fi are three main candidate technologies for pedestrian navigation. GPS cannot provide reliable pedestrian navigation indoors because the GPS signals are degraded in indoor environments. MEMS sensors provide accurate but relative positioning solution for short time periods of standalone usages because of MEMS sensors’ errors characteristics. Wi-Fi can provide absolute location information by using pre-existing wireless infrastructures. However, Wi-Fi positioning requires special radio maps databases which is labour intensive and time consuming. Moreover, accurate orientation information from Wi-Fi cannot be provided in any of the existing methods. This research aims to improve the accuracy, reliability and accessibility of indoor navigation in two different ways for the current smart phones using GPS, Wi-Fi and MEMS sensors. Firstly, an algorithm is proposed to estimate the Wi-Fi heading by using clustering based and estimation based techniques. Secondly, when the GPS/MEMS sensors position solution is accurate, it is used to build the database for Wi-Fi positioning. Once the database is ready, Wi-Fi positioning is combined with the GPS/ MEMS sensors solution to provide the final robust positioning solution. The two algorithms will be used in the T-PN, the Trusted POSitioning Inc.'s Trusted Portable Navigator, for different real time systems such as Windows and Android based smart phones. The result will be a more robust and accurate navigation solution for deep indoor environments.

Faculty Supervisor:

Dr. Naser El-Sheimy

Student:

Yuan Zhuang

Partner:

Trusted Positioning Inc.

Discipline:

Engineering

Sector:

Information and communications technologies

University:

University of Calgary