{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:44Z","timestamp":1760242604789,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T00:00:00Z","timestamp":1511222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors\u2019 locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine.<\/jats:p>","DOI":"10.3390\/s17112690","type":"journal-article","created":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T11:23:20Z","timestamp":1511263400000},"page":"2690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements"],"prefix":"10.3390","volume":"17","author":[{"given":"Slavisa","family":"Tomic","sequence":"first","affiliation":[{"name":"ISR\/IST, LARSyS, Universidade de Lisboa, 1049-001 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7315-8739","authenticated-orcid":false,"given":"Marko","family":"Beko","sequence":"additional","affiliation":[{"name":"CICANT-CIC.DIGITAL, Universidade Lus\u00f3fona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal"},{"name":"CTS\/UNINOVA, Campus da FCT\/UNL, Monte de Caparica, 2829-516 Caparica, Portugal"}]},{"given":"Rui","family":"Dinis","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Av. Rovisco Pais 1, Torre Norte, piso 10, 1049-001 Lisboa, Portugal"},{"name":"Dep.o de Eng.a Electrot\u00e9cnica, FCT\/UNL, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3524-5556","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Gomes","sequence":"additional","affiliation":[{"name":"ISR\/IST, LARSyS, Universidade de Lisboa, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MSP.2005.1458287","article-title":"Locating the Nodes: Cooperative Localization in Wireless Sensor Networks","volume":"22","author":"Patwari","year":"2005","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TVT.2015.2403868","article-title":"Indoor Tracking: Theory, Methods, and Technologies","volume":"64","author":"Dardari","year":"2016","journal-title":"IEEE Trans. Veh. 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