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Adaptive sampling in autonomous marine sensor networks

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dc.contributor.advisor Henrik Schmidt. en_US
dc.contributor.author Eickstedt, Donald Patrick en_US
dc.contributor.other Woods Hole Oceanographic Institution. en_US
dc.date.accessioned 2007-10-19T20:31:13Z
dc.date.available 2007-10-19T20:31:13Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/39207
dc.description Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2006. en_US
dc.description Includes bibliographical references (leaves 209-213). en_US
dc.description.abstract In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. en_US
dc.description.abstract (cont.) In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks. en_US
dc.description.statementofresponsibility by Donald Patrick Eickstedt. en_US
dc.format.extent 213 leaves en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject /Woods Hole Oceanographic Institution. Joint Program in Oceanography/Applied Ocean Science and Engineering. en_US
dc.subject Mechanical Engineering. en_US
dc.subject Woods Hole Oceanographic Institution. en_US
dc.title Adaptive sampling in autonomous marine sensor networks en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Joint Program in Oceanography/Applied Ocean Science and Engineering. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Mechanical Engineering. en_US
dc.contributor.department Woods Hole Oceanographic Institution. en_US
dc.identifier.oclc 76882061 en_US


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