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This thesis provides a novel approach to the problem of state estimation for discrete-time nonlinear systems in the presence of large model uncertainties. Though classical nonlinear Kalman filters such as the extended Kalman filter (EKF) can handle uncertainties by increasing the value of noise covariances, this is only applicable to systems with small uncertainties. To this end, a discretetime robust extended Kalman filter (REKF) is formulated and applied to examples from the fields of aerospace engineering and signal processing with an emphasis on attitude estimation for small unmanned aerial vehicles (UAVs) and image processing under the influence of atmospheric turbulence. The robust filter is an approximate set-valued state estimator where the Riccati and filter equations are obtained as an approximate solution to a reverse-time optimal control problem defining the set-valued state estimator. The advantages of the REKF over the classical EKF are investigated for examples from the fields aerospace engineering and signal processing where large model uncertainties are introduced. In the case of small UAVs, an alternative attitude estimation algorithm based on the REKF is proposed in the event of gyroscopic failure and the inability of the vehicle to carry redundant sensors due to limited payload capabilities. In the case of image reconstruction under atmospheric turbulence, a robust pixel-wandering (random shifts) scheme is proposed to aid the process of image reconstruction. Also, problems pertaining to platform vibration analysis for aerospace vehicles and a frequency demodulation process in the presence of channel-induced uncertainties is also discussed.

Includes bibliographical reference. / viii, 149 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This study is concerned with filters that are robust to uncertainties in either the signal models or the noise statistics. Extensions to an interpolation approach to solving a continuous-time, linear, stationary filtering problem are presented. A robust extended Kalman filter is developed. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1996?

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 92-93).

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xii, 108, [9] p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 104-108).

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains x, 141 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 76-78).

Thesis (Ph. D.)--University of Sydney, 2002. / Title from title screen (viewed Apr. 24, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Australian Centre for Field Robotics, Dept. of Mechanical and Mechatronic Engineering, Faculty of Engineering. Degree awarded 2002; thesis submitted 2001. Includes bibliography. Also available in print form.

Thesis (M.App.Sc.) - Carleton University, 2006. / Includes bibliographical references (p. 111-118). Also available in electronic format on the Internet.

Thesis (Ph.D.) -- University of Tulsa, 2008. / Includes bibliographical references (leaves 261-270)

Thesis (Ph.D.)--University of Wollongong, 2008. / Typescript. Includes bibliographical references: leaf 243-250.