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On-Line RSSI-Range Model Learning for Target Localization and Tracking

Opened Access On-Line RSSI-Range Model Learning for Target Localization and Tracking

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Autor: Martínez de Dios, José Ramiro
Ollero Baturone, Aníbal
Fernández Jiménez, Francisco José
Regoli, Carolina
Departamento: Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
Universidad de Sevilla. Departamento de Ingeniería Telemática
Fecha: 2017-08
Publicado en: Journal of Sensor and Actuators Networks, 6 (3), 15-.
Tipo de documento: Artículo
Resumen: The interactions of Received Signal Strength Indicator (RSSI) with the environment are very difficult to be modeled, inducing significant errors in RSSI-range models and highly disturbing target localization and tracking methods. Some techniques adopt a training-based approach in which they off-line learn the RSSI-range characteristics of the environment in a prior training phase. However, the training phase is a time-consuming process and must be repeated in case of changes in the environment, constraining flexibility and adaptability. This paper presents schemes in which each anchor node on-line learns its RSSI-range models adapted to the particularities of its environment and then uses its trained model for target localization and tracking. Two methods are presented. The first uses the information of the location of anchor nodes to dynamically adapt the RSSI-range model. In the second one, each anchor node uses estimates of the target location –anchor nodes are assumed equipped wit...
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Cita: Martínez de Dios, J.R., Ollero Baturone, A., Fernández Jiménez, F.J. y Regoli, C. (2017). On-Line RSSI-Range Model Learning for Target Localization and Tracking. Journal of Sensor and Actuators Networks, 6 (3), 15-.
Tamaño: 3.996Mb
Formato: PDF

URI: http://hdl.handle.net/11441/64064

DOI: 10.3390/jsan6030015

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