{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:36Z","timestamp":1760242416776,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,8,10]],"date-time":"2017-08-10T00:00:00Z","timestamp":1502323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>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 \u2013anchor nodes are assumed equipped with cameras\u2014to 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.<\/jats:p>","DOI":"10.3390\/jsan6030015","type":"journal-article","created":{"date-parts":[[2017,8,10]],"date-time":"2017-08-10T10:34:35Z","timestamp":1502361275000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["On-Line RSSI-Range Model Learning for Target Localization and Tracking"],"prefix":"10.3390","volume":"6","author":[{"given":"Jos\u00e9","family":"Ramiro Mart\u00ednez-de Dios","sequence":"first","affiliation":[{"name":"Robotics Vision and Control Group, Universidad de Sevilla, Escuela Superior de Ingenieros, c\/Camino de los Descubrimientos s\/n, 41092 Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anibal","family":"Ollero","sequence":"additional","affiliation":[{"name":"Robotics Vision and Control Group, Universidad de Sevilla, Escuela Superior de Ingenieros, c\/Camino de los Descubrimientos s\/n, 41092 Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Universidad de Sevilla, Escuela Superior de Ingenieros, c\/Camino de los Descubrimientos s\/n, 41092 Seville, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carolina","family":"Regoli","sequence":"additional","affiliation":[{"name":"Central University of Venezuela, Ciudad Universitaria s\/n, Caracas 1050, Venezuela"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34:1","DOI":"10.1145\/2240116.2240123","article-title":"Radio link quality estimation in wireless sensor networks: A survey","volume":"8","author":"Baccour","year":"2012","journal-title":"ACM Trans. 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