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Daniela Rus and John Fisher. Russ, John A., S.M. Massachusetts Institute of Technology Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2011-04-25T14:16:11Z 2011-04-25T14:16:11Z 2010 2010 http://hdl.handle.net/1721.1/62314 711000682 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (p. 125-127). In order to utilize mobile sensor nodes in a sensing and estimation problem, one must carefully consider the optimal placement of those sensor nodes and simultaneously account for the cost incurred in moving the sensor nodes. We present an approximate dynamic programming approach to a tracking problem with mobile sensor nodes. We utilize mutual information as the objective for optimal sensor placement. We show how a constrained dynamic programming approach allows us to balance estimation quality against mobility costs. However this constrained optimization problem is NP-hard. We present a set of approximations that allow this dynamic program to be solved with polynomial complexity in the number of sensors. We present a greedy multiple time step planning algorithm that greedily selects the most informative paths over a fixed planning horizon. These approximation algorithms are verified via simulation to give a comparative analysis of estimate quality and mobility costs. by John A. Russ. S.M. 127 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Mutual information based tracking with mobile sensors Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8ee2babc-69e4-4653-85d4-c8bb15d85369"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Mutual information based tracking with mobile sensors</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Russ, John A., S.M. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In order to utilize mobile sensor nodes in a sensing and estimation problem, one must carefully consider the optimal placement of those sensor nodes and simultaneously account for the cost incurred in moving the sensor nodes. We present an approximate dynamic programming approach to a tracking problem with mobile sensor nodes. We utilize mutual information as the objective for optimal sensor placement. We show how a constrained dynamic programming approach allows us to balance estimation quality against mobility costs. However this constrained optimization problem is NP-hard. We present a set of approximations that allow this dynamic program to be solved with polynomial complexity in the number of sensors. We present a greedy multiple time step planning algorithm that greedily selects the most informative paths over a fixed planning horizon. These approximation algorithms are verified via simulation to give a comparative analysis of estimate quality and mobility costs.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>