Target tracking in a multisensor environment using neural networks

Persistent Link:
http://hdl.handle.net/10150/298748
Title:
Target tracking in a multisensor environment using neural networks
Author:
Wong, Yee Chin
Issue Date:
2000
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:
The advent of technology has brought to the field of engineering many tools that were once considered impractical. For example, the increased processing speed of microprocessors now allows measurements from image sensors to be used for target tracking or target identification in real time--a task once thought unachievable. Of late the advances made in artificial intelligence (AI), specifically the artificial neural network, have sprung many different applications among which the implementation of AI controllers being the most popular. However, these advances have been slow in their implementation in the field of target tracking for several reasons. First, there seems to be a lack of sound tracking architectures that can exploit the use of artificial intelligent agents. Second, there is some difficulty in fusing the different forms of information that can be measured from the various available sensors such as the image sensor, millimeter wave radar, Doppler radar, etc. Third, the increased computational complexity due to the employment of the various sensors could limit the practical usefulness of such a tracking system. This dissertation presents a novel framework in which various dissimilar sensors can be used simultaneously to track a highly agile and non-cooperative target. The proposed framework not only allows the usage of multiple sensors to yield a robust and accurate tracker but also maintain a reasonable computational requirement. Unlike the methods proposed in the literature for the design of multi-sensor tracking systems, this dissertation presents an AI-based system that can accept, process, and fuse measurements from any number of sensors of dissimilar forms. The principal contributions of this dissertation are the following: (i) a novel architecture of a three-layer feedforward neural-network-based tracking system with the ability to fuse measurements from dissimilar sensors; (ii) a powerful optimization algorithm for training the neural network; (iii) a novel mathematical target motion model to simplify the training and implementation of the proposed tracking system.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Artificial Intelligence.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Sundareshan, Malur K.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleTarget tracking in a multisensor environment using neural networksen_US
dc.creatorWong, Yee Chinen_US
dc.contributor.authorWong, Yee Chinen_US
dc.date.issued2000en_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.abstractThe advent of technology has brought to the field of engineering many tools that were once considered impractical. For example, the increased processing speed of microprocessors now allows measurements from image sensors to be used for target tracking or target identification in real time--a task once thought unachievable. Of late the advances made in artificial intelligence (AI), specifically the artificial neural network, have sprung many different applications among which the implementation of AI controllers being the most popular. However, these advances have been slow in their implementation in the field of target tracking for several reasons. First, there seems to be a lack of sound tracking architectures that can exploit the use of artificial intelligent agents. Second, there is some difficulty in fusing the different forms of information that can be measured from the various available sensors such as the image sensor, millimeter wave radar, Doppler radar, etc. Third, the increased computational complexity due to the employment of the various sensors could limit the practical usefulness of such a tracking system. This dissertation presents a novel framework in which various dissimilar sensors can be used simultaneously to track a highly agile and non-cooperative target. The proposed framework not only allows the usage of multiple sensors to yield a robust and accurate tracker but also maintain a reasonable computational requirement. Unlike the methods proposed in the literature for the design of multi-sensor tracking systems, this dissertation presents an AI-based system that can accept, process, and fuse measurements from any number of sensors of dissimilar forms. The principal contributions of this dissertation are the following: (i) a novel architecture of a three-layer feedforward neural-network-based tracking system with the ability to fuse measurements from dissimilar sensors; (ii) a powerful optimization algorithm for training the neural network; (iii) a novel mathematical target motion model to simplify the training and implementation of the proposed tracking system.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectArtificial Intelligence.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSundareshan, Malur K.en_US
dc.identifier.proquest9972134en_US
dc.identifier.bibrecord.b4064294xen_US
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