Hybrid and Cooperative Positioning Solutions for Wireless Networks

In this thesis, some hybrid and cooperative solutions are proposed and analyzed to locate the user in challenged scenarios, with the aim to overcome the limits of positioning systems based on single technology. The proposed approaches add hybrid and cooperative features to some conventional position estimation techniques like Kalman filter and particle filter, and fuse information from different radio frequency technologies. The concept of cooperative positioning is enhanced with hybrid technologies, in order to further increase the positioning accuracy and availability. In particular, wireless sensor networks and radio frequency identification technology are used together to enhance the collected data with position information. Terrestrial ranging techniques (i.e., ultra-wide band technology) are employed to assist the satellite-based localization in urban canyons and indoors. Moreover, some advanced positioning algorithms, such as energy efficient, cognitive tracking and non-line-of-sight identification, are studied to satisfy the different positioning requirements in harsh indoor environments. The proposed hybrid and cooperative solutions are tested and verified by first Monte Carlo simulations then real experiments. The obtained results demonstrate that the proposed solutions can increase the robustness (positioning accuracy and availability) of the current localization system

A Cognitive and Cooperative Tracking Approach in Wireless Networks

This paper presents a novel cognitive and cooperative tracking (CCT) approach based on extended Kalman filter (EKF) to localize mobile nodes in wireless networks. The proposed algorithm shows three important features: energy efficient, cognitive and cooperative. More specifically, the tracking algorithm adaptively adjusts the transmission power to optimize the energy consumption while meeting the required localization accuracy Pa imposed by a generic application. Moreover, it adopts a self-learning scheme to track the time-variant environment's characteristics (e.g., range measurement noise) and use this knowledge to improve tracking performance. Finally, the algorithm exploits the cooperation among unknown nodes that leads to further improved performance and reduced power consumption. Simulation results show that the proposed CCT approach is able to improve positioning performance and meet the required accuracy Pa while energy consumption is optimize