Position estimation and tracking of an autonomous mobile sensor using received signal strength Black, Timothy J., Pathirana, Pubudu and Nahavandi, Saeid 2008, Position estimation and tracking of an autonomous mobile sensor using received signal strength, in ISSNIP 2008 : IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE, Piscataway, N.J., pp. 19-24, doi: 10.1109/ISSNIP.2008.4761956. Attached Files Name Description MIMEType Size Downloads black-positionestimation-2008.pdf Published version application/pdf 438.08KB 256 Title Position estimation and tracking of an autonomous mobile sensor using received signal strength Author(s) Black, Timothy J. Pathirana, Pubudu orcid.org/0000-0001-8014-7798 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name IEEE Conference on Intelligent Sensors, Sensor Networks and Information Processing (2008 : Sydney, N.S.W.) Conference location Sydney, N.S.W. Conference dates 15-18 December 2008 Title of proceedings ISSNIP 2008 : IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing Editor(s) Bouzerdoum, A. Palaniswami, M. Dissanayake, G. Sowmya, A. Publication date 2008 Conference series IEEE Conference on Intelligent Sensors, Sensor Networks and Information Processing Start page 19 End page 24 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Summary In this paper, an algorithm for approximating the path of a moving autonomous mobile sensor with an unknown position location using Received Signal Strength (RSS) measurements is proposed. Using a Least Squares (LS) estimation method as an input, a Maximum-Likelihood (ML) approach is used to determine the location of the unknown mobile sensor. For the mobile sensor case, as the sensor changes position the characteristics of the RSS measurements also change; therefore the proposed method adapts the RSS measurement model by dynamically changing the pass loss value alpha to aid in position estimation. Secondly, a Recursive Least-Squares (RLS) algorithm is used to estimate the path of a moving mobile sensor using the Maximum-Likelihood position estimation as an input. The performance of the proposed algorithm is evaluated via simulation and it is shown that this method can accurately determine the position of the mobile sensor, and can efficiently track the position of the mobile sensor during motion. Notes ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ISBN 9781424429578 Language eng DOI 10.1109/ISSNIP.2008.4761956 Field of Research 080503 Networking and Communications Socio Economic Objective 861799 Communication Equipment not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2008, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30018212 Document type: Conference Paper Collections: School of Engineering and Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 14 Aug 2009, 14:06:04 EST Thu, 10 Dec 2009, 07:13:36 EST Thu, 10 Dec 2009, 07:15:30 EST Fri, 14 May 2010, 16:03:34 EST Fri, 14 May 2010, 16:04:42 EST Tue, 07 Sep 2010, 15:05:50 EST Fri, 16 Mar 2012, 17:05:08 EST Mon, 13 Jan 2014, 10:19:51 EST Wed, 21 Oct 2015, 16:52:13 EST Tue, 06 Sep 2016, 12:56:55 EST Tue, 05 Dec 2017, 06:32:10 EST Fri, 07 Sep 2018, 14:20:46 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 827 Abstract Views, 277 File Downloads  -  Detailed Statistics Created: Fri, 14 Aug 2009, 14:06:04 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.