On-Line RSSI-Range Model Learning for 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...

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Bibliographic Details
Published inJournal of sensor and actuator networks Vol. 6; no. 3; p. 15
Main Authors Ramiro Martínez-de Dios, José, Ollero, Anibal, Fernández, Francisco, Regoli, Carolina
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2017
Subjects
Anchors
Localization
RSSI
Signal strength
Tracking
Training
Wireless Sensor Network
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Summary: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.
ISSN:2224-2708
2224-2708
DOI:10.3390/jsan6030015