4 research outputs found

    Censoring for Bayesian Cooperative Positioning in Dense Wireless Networks

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    Cooperative positioning studies based on WLANs

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    Location information and location-based service have gained importance in recent years because, based on their concept, a new business market has been opened which encompass emergency services, security, monitoring, tracking, logistics, etc. Nowadays, the most developed positioning systems, namely the Global Navigation Satellite Systems (GNSS), are meant for outdoor use. In order to integrate outdoor and indoor localization in the same mobile application, several lines of research have been created for the purpose of investigating the possibility of wireless network technologies and of overcoming the challenges faced by GNSS in performing localization and navigation in indoor environments. The benefit in using wireless networks is that they provide a minimally invasive solution which is based on software algorithms that can be implemented and executed in the Mobile Station (MS) or in a Location Server connected to the network. This thesis focuses on the development of localization approaches based on Received Signal Strength (RSS) and applied in WLANs. Such approaches demonstrated in recent research advances that RSS-based localization algorithms are the simplest existing approaches due to the fact that the RSSs are most accessible existing measurements. RSS measurements can be used with two main algorithms, which are addressed in this thesis: Fingerprinting method (FP) and Pathloss method (PL). These two methods can be applied in both cooperative and non-cooperative algorithms. Such algorithms are evaluated here in terms of Root Mean Square Error (RMSE) for both simulated and real-field data
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