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dc.contributor.supervisorSharma, Sanjay
dc.contributor.authorMotwani, Amit
dc.contributor.otherFaculty of Science and Engineeringen_US
dc.date.accessioned2015-06-12T09:58:00Z
dc.date.available2015-06-12T09:58:00Z
dc.date.issued2015
dc.date.issued2015
dc.identifier10356169en_US
dc.identifier.urihttp://hdl.handle.net/10026.1/3368
dc.descriptionIn reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.en_US
dc.description.abstract

This thesis is about a robust filtering method known as the interval Kalman filter (IKF), an extension of the Kalman filter (KF) to the domain of interval mathematics. The key limitation of the KF is that it requires precise knowledge of the system dynamics and associated stochastic processes. In many cases however, system models are at best, only approximately known. To overcome this limitation, the idea is to describe the uncertain model coefficients in terms of bounded intervals, and operate the filter within the framework of interval arithmetic. In trying to do so, practical difficulties arise, such as the large overestimation of the resulting set estimates owing to the over conservatism of interval arithmetic. This thesis proposes and demonstrates a novel and effective way to limit such overestimation for the IKF, making it feasible and practical to implement. The theory developed is of general application, but is applied in this work to the heading estimation of the Springer unmanned surface vehicle, which up to now relied solely on the estimates from a traditional KF. However, the IKF itself simply provides the range of possible vehicle headings. In practice, the autonomous steering system requires a single, point-valued estimate of the heading. In order to address this requirement, an innovative approach based on the use of machine learning methods to select an adequate point-valued estimate has been developed. In doing so, the so called weighted IKF (wIKF) estimate provides a single heading estimate that is robust to bounded model uncertainty. In addition, in order to exploit low-cost sensor redundancy, a multi-sensor data fusion algorithm compatible with the wIKF estimates and which additionally provides sensor fault tolerance has been developed. All these techniques have been implemented on the Springer platform and verified experimentally in a series of full-scale trials, presented in the last chapter of the thesis. The outcomes demonstrate that the methods are both feasible and practicable, and that they are far more effective in providing accurate estimates of the vehicle’s heading than the conventional KF when there is uncertainty in the system model and/or sensor failure occurs.

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dc.description.sponsorshipEPSRCen_US
dc.language.isoenen_US
dc.publisherPlymouth Universityen_US
dc.subjectInterval Kalman filteren_US
dc.subjectUnmanned surface vehicleen_US
dc.subjectRobust estimationen_US
dc.titleInterval Kalman Filtering Techniques for Unmanned Surface Vehicle Navigationen_US
dc.typeThesis
plymouth.versionFull versionen_US
dc.identifier.doihttp://dx.doi.org/10.24382/1491


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