MATCHED WAVEFORM DESIGN AND ADAPTIVE BEAMSTEERING IN COGNITIVE RADAR APPLICATIONS

Persistent Link:
http://hdl.handle.net/10150/194499
Title:
MATCHED WAVEFORM DESIGN AND ADAPTIVE BEAMSTEERING IN COGNITIVE RADAR APPLICATIONS
Author:
Romero, Ric
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:
Cognitive Radar (CR) is a paradigm shift from a traditional radar system in that previous knowledge and current measurements obtained from the radar channel are used to form a probabilistic understanding of its environment. Moreover, CR incorporates this probabilistic knowledge into its task priorities to form illumination and probing strategies thereby rendering it a closed-loop system. Depending on the hardware's capabilities and limitations, there are various degrees of freedom that a CR may utilize. Here we will concentrate on two: temporal, where it is manifested in adaptive waveform design; and spatial, where adaptive beamsteering is used for search-and-track functions. This work is divided into three parts. First, comprehensive theory of SNR and mutual information (MI) matched waveform design in signal-dependent interference is presented. Second, these waveforms are used in a closed-loop radar platform performing target discrimination and target class identification, where the extended targets are either deterministic or stochastic. The CR's probabilistic understanding is updated via the Bayesian framework. Lastly, we propose a multiplatform CR network for integrated search-and-track application. The two radar platforms cooperate in developing a four-dimensional probabilistic understanding of the channel. The two radars also cooperate in forming dynamic spatial illumination strategy, where beamsteering is matched to the channel uncertainty to perform the search function. Once a target is detected and a track is initiated, track information is integrated into the beamsteering strategy as part of CR's task prioritization.
Type:
text; Electronic Dissertation
Keywords:
Beamsteering; Cognitive Radar; Cognitive Radar Network; Matched Illumination; Target Recogniiton; Track and Search
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Goodman, Nathan
Committee Chair:
Goodman, Nathan

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleMATCHED WAVEFORM DESIGN AND ADAPTIVE BEAMSTEERING IN COGNITIVE RADAR APPLICATIONSen_US
dc.creatorRomero, Ricen_US
dc.contributor.authorRomero, Ricen_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.abstractCognitive Radar (CR) is a paradigm shift from a traditional radar system in that previous knowledge and current measurements obtained from the radar channel are used to form a probabilistic understanding of its environment. Moreover, CR incorporates this probabilistic knowledge into its task priorities to form illumination and probing strategies thereby rendering it a closed-loop system. Depending on the hardware's capabilities and limitations, there are various degrees of freedom that a CR may utilize. Here we will concentrate on two: temporal, where it is manifested in adaptive waveform design; and spatial, where adaptive beamsteering is used for search-and-track functions. This work is divided into three parts. First, comprehensive theory of SNR and mutual information (MI) matched waveform design in signal-dependent interference is presented. Second, these waveforms are used in a closed-loop radar platform performing target discrimination and target class identification, where the extended targets are either deterministic or stochastic. The CR's probabilistic understanding is updated via the Bayesian framework. Lastly, we propose a multiplatform CR network for integrated search-and-track application. The two radar platforms cooperate in developing a four-dimensional probabilistic understanding of the channel. The two radars also cooperate in forming dynamic spatial illumination strategy, where beamsteering is matched to the channel uncertainty to perform the search function. Once a target is detected and a track is initiated, track information is integrated into the beamsteering strategy as part of CR's task prioritization.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectBeamsteeringen_US
dc.subjectCognitive Radaren_US
dc.subjectCognitive Radar Networken_US
dc.subjectMatched Illuminationen_US
dc.subjectTarget Recogniitonen_US
dc.subjectTrack and Searchen_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, Nathanen_US
dc.contributor.chairGoodman, Nathanen_US
dc.contributor.committeememberGehm, Michaelen_US
dc.contributor.committeememberRyan, Williamen_US
dc.identifier.proquest11299en_US
dc.identifier.oclc752261145en_US
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