Artículo
On-Line RSSI-Range Model Learning for Target Localization and Tracking
Autor/es | 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 de publicación | 2017-08 |
Fecha de depósito | 2017-08-29 |
Publicado en |
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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 ... 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 with cameras—to on-line adapt its RSSI-range model. The paper presents both methods, describes their operation integrated in localization and tracking schemes and experimentally evaluates their performance in the UBILOC testbed |
Identificador del proyecto | H2020-ICT-2016-2017/H2020-ICT-2016-1
info:eu-repo/grantAgreement/EC/H2020/644271 DPI2014-59383-C2-1-R info:eu-repo/grantAgreement/EC/H2020/687384 |
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-. |
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