INS/GPS integration using neural networks for land vehicular navigation applications

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dc.contributor.advisorEl-Sheimy, Naser
dc.contributor.authorChiang, Kai-Wei
dc.date.accessioned2005-08-16T16:53:26Z
dc.date.available2005-08-16T16:53:26Z
dc.date.issued2004
dc.descriptionBibliography: p. 232-244en
dc.description.abstractMost of the positioning technologies for modern land vehicular navigation systems have been available for 25 years. Virtually all of the systems augment two or more of these technologies. Typical candidates for an integrated navigation system are the Global Position System (GPS) and Inertial Navigation Systems (INS). The Kalman filter has been widely adopted as an optimal estimation tool for the INS/GPS integration, however, several limitations of such multi-sensor integration methodology have been reported; such as the impact of INS short term errors, model dependency, prior knowledge dependency, sensor dependency, and linearization dependency. To reduce the impact of short term INS sensor errors, the bandwidth of true motion dynamics were identified by spectrum analysis and the first generation denoising algorithm that used the Discrete Wavelet Transform (DWT) was applied to identify the limitations of the existing denoising algorithm. Consequently, this research proposed the cascade denoising algorithm to overcome the limitations of existing denoising algorithms. It was then evaluated using several INS/GPS integrated land vehicular systems and the results demonstrated superior performance to existing denoising algorithms in both the positioning and spectrum domains. In addition, the impact of proposed algorithms on different integrated systems was investigated extensively. Furthermore, an alternative INS/GPS integration methodology, the conceptual intelligent navigator incorporating artificial intelligence techniques, was proposed to reduce the remaining limitations of traditional navigators that use the Kalman filter approach. The proposed conceptual intelligent navigator consisted of several different INS/GPS integration architectures that were developed using artificial neural networks to acquire the navigation knowledge. In addition, the "brain", a navigation information database, and a window based weight updating scheme were implemented to store and accumulate navigation knowledge. The conceptual intelligent navigator was evaluated using several INS/GPS integrated land vehicular systems and the results demonstrated superior performance to traditional navigator in the position domain. Finally, a low cost INS/GPS integrated system was considered to verify the advantages gained by incorporating the conceptual intelligent navigator as an alternative method toward developing next generation land vehicular navigation systems.en
dc.format.extentxxvi, 280 leaves : ill. ; 30 cm.en
dc.identifier.citationChiang, K. (2004). INS/GPS integration using neural networks for land vehicular navigation applications (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/23670en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/23670
dc.identifier.isbn0494045892en
dc.identifier.lccAC1 .T484 2004 C493en
dc.identifier.urihttp://hdl.handle.net/1880/41409
dc.language.isoeng
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.titleINS/GPS integration using neural networks for land vehicular navigation applications
dc.typedoctoral thesis
thesis.degree.disciplineGeomatics Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
ucalgary.thesis.accessionTheses Collection 58.002:Box 1494 520492011
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
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