INFORMATION-THEORETIC OPTIMIZATION OF WIRELESS SENSOR NETWORKS AND RADAR SYSTEMS

Persistent Link:
http://hdl.handle.net/10150/193668
Title:
INFORMATION-THEORETIC OPTIMIZATION OF WIRELESS SENSOR NETWORKS AND RADAR SYSTEMS
Author:
Kim, Hyoung-soo
Issue Date:
2010
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Three information measures are discussed and used as objective functions for optimization of wireless sensor networks (WSNs) and radar systems. In addition, a long-term system performance measure is developed for evaluating the performance of slow-fading WSNs. Three system applications are considered: a distributed detection system, a distributed multiple hypothesis system, and a radar target recognition system.First, we consider sensor power optimization for distributed binary detection systems. The system communicates over slow-fading orthogonal multiple access channels. In earlier work, it was demonstrated that system performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system. We define outage probability for slow-fading system as a long-term performance measure, and analytically develop the detection outage with the given system model.Based on the analytical result of the outage probability, diversity gain is derived and shown to be proportional to the number of the sensor nodes. Then, we extend the optimized power control strategy to a distributed multiple hypothesis system, and enhance the power optimization by exploiting a priori probabilities and local sensor statistics. We also extend outage probability to the distributed multiple-hypotheses problem. The third application is radar waveform design with a new performance measure: Task-Specific Information (TSI). TSI is an information-theoretic measure formulated for one or more specific sensor tasks by encoding the task(s) directly into the signal model via source variables. For example, we consider the problem of correctly classifying a linear system from a set of known alternatives, and the source variable takes the form of an indicator vector that selects the transfer function of the true hypothesis. We then compare the performance of TSI with conventional waveforms and other information-theoretic waveform designs via simulation. We apply radar-specific constraints and signal models to the waveform optimization.
Type:
text; Electronic Dissertation
Keywords:
Information measure; Power optimization; Radar system; Waveform design; Wireless communication; Wireless sensor network
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Goodman, Nathan A.
Committee Chair:
Goodman, Nathan A.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleINFORMATION-THEORETIC OPTIMIZATION OF WIRELESS SENSOR NETWORKS AND RADAR SYSTEMSen_US
dc.creatorKim, Hyoung-sooen_US
dc.contributor.authorKim, Hyoung-sooen_US
dc.date.issued2010en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThree information measures are discussed and used as objective functions for optimization of wireless sensor networks (WSNs) and radar systems. In addition, a long-term system performance measure is developed for evaluating the performance of slow-fading WSNs. Three system applications are considered: a distributed detection system, a distributed multiple hypothesis system, and a radar target recognition system.First, we consider sensor power optimization for distributed binary detection systems. The system communicates over slow-fading orthogonal multiple access channels. In earlier work, it was demonstrated that system performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system. We define outage probability for slow-fading system as a long-term performance measure, and analytically develop the detection outage with the given system model.Based on the analytical result of the outage probability, diversity gain is derived and shown to be proportional to the number of the sensor nodes. Then, we extend the optimized power control strategy to a distributed multiple hypothesis system, and enhance the power optimization by exploiting a priori probabilities and local sensor statistics. We also extend outage probability to the distributed multiple-hypotheses problem. The third application is radar waveform design with a new performance measure: Task-Specific Information (TSI). TSI is an information-theoretic measure formulated for one or more specific sensor tasks by encoding the task(s) directly into the signal model via source variables. For example, we consider the problem of correctly classifying a linear system from a set of known alternatives, and the source variable takes the form of an indicator vector that selects the transfer function of the true hypothesis. We then compare the performance of TSI with conventional waveforms and other information-theoretic waveform designs via simulation. We apply radar-specific constraints and signal models to the waveform optimization.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectInformation measureen_US
dc.subjectPower optimizationen_US
dc.subjectRadar systemen_US
dc.subjectWaveform designen_US
dc.subjectWireless communicationen_US
dc.subjectWireless sensor networken_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGoodman, Nathan A.en_US
dc.contributor.chairGoodman, Nathan A.en_US
dc.contributor.committeememberBilgin, Alien_US
dc.contributor.committeememberDjordjevic, Ivanen_US
dc.identifier.proquest11257en_US
dc.identifier.oclc752261095en_US
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