Automatic Training and Radiomap Collection for Indoor Location Estimation

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Mapsted is a Canadian Technology company which specializes in indoor positioning services. They have developed**a core localization technology which provides real-time indoor positioning. The goal of this project is to develop**further enhancements to a specific module of their core localization technology. More specifically, this project will**develop new algorithms for automatic construction of system calibration for indoor localization. We will use graph**theory to build a radio map with unsupervised measurements of WiFi received signal strength (RSS) and magnetic**field (MF). When a user with the downloaded application visits an area of interest, his/her phone captures WiFi**RSS from available access points as well as the MF obtained from the phones magnetometer, and transmits to a**central server. Such data do not have any location attached to the measured RSS. However, the RSS values,**captured by the user, are also naturally constrained by the geometry of the environment and structural**limitations. In practice, there exist some correlation among the different RSS measurements. For example, the**RSS values collected in proximity of each other will likely share a large number of visible access points with**similar RSS values. In this project, we will use the correlation between pairs of RSS measurements to graft traces**to build a data graph, which is indeed an arrangement of RSS values that are structured to create a new**graphical representation of the RSS measurements. We will then propose algorithms to overlap the data graph**with the floor plan of the environment.