Learning Based Autonomous Indoor Localization and Map Generation

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This CRD project proposes to build a fully autonomous indoor localization and mapping system using machine learning based fingerprinting. Location based services are considered by the ICT industry as a significant permissive technology and becoming a vital part of life. It has wide applications in navigation, tracking, healthcare, emergency and public safety, travel and hospitality, aviation, retail and advertising, oil and mining, manufacturing, etc. With the growing deployment of Internet-of-Things (IoT) sensors and devices, the value of location information multiplies even faster as new ideas in the mobile phone and IoT markets bring new application scenarios. Indoor localization, however, is challenging to realize with accuracy. Conventional ranging based techniques, using the received signal strength (RSS), time of arrival or angle of arrival of wireless signals, are highly dependent on the complex and dynamically changing physical propagation environment. ** In this project, we use location fingerprinting (LF) to realize indoor positioning. The basic idea behind LF is that the multipath structure of the channel is unique to every location and can be considered as a fingerprint of location. Due to the ubiquity of wireless local area network infrastructure, we use the RSS of WiFi as the fingerprint of a radio map. Our first objective is to develop a location learning process that can accurately determine the location of a WiFi user in a building. The second objective is to design a robot enabled automatic site survey system that creates a WiFi radio map as well as the building site map without human intervention and the need for AP location or floor plan. The site survey provides the database for LF and automation greatly reduces the human effort needed which is a major hurdle to practical adoption. The expected outcome of the project will provide a working prototype to Fortinet, which can be transferred to their product line. The technologies developed can also be adopted by Canadian high-tech industries to enhance their product offering and benefit end users. ****************