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EnglishThis 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 optimized.
A Cognitive and Cooperative Tracking Approach in Wireless Networks / Zhoubing Xiong; Mingbo Dai; Francesco Sottile; Maurizio A. Spirito; Roberto Garello. - (2013). ((Intervento presentato al convegno IEEE International Conference on Communications (ICC tenutosi a Budapest, Hungary nel June 2013 [10.1109/ICC.2013.6654948].
| Titolo: | A Cognitive and Cooperative Tracking Approach in Wireless Networks | |
| Autori: | ||
| Data di pubblicazione: | 2013 | |
| Abstract: | This paper presents a novel cognitive and cooperative tracking (CCT) approach based on extended K...alman 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 optimized. | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2591609
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