Design of finite memory filters

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dc.contributor.authorXu, Haoen_US
dc.date.accessioned2024-08-15T20:18:55Z
dc.date.available2024-08-15T20:18:55Z
dc.date.copyright1989en_US
dc.date.issued1989
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en
dc.description.abstractThis thesis deals with the design of finite memory filters for both the one and the two dimensional cases. Finite memory filters have many applications in real time control, es­timation and identification, particularly in cases where the information about the system dynamics and the noise statistics is not precisely known. A re­cursive finite memory filter is developed to fit an nth order polynomial to equally spaced measurements. The parameters of the filter are determined by applying a weighted least-squares performance index, and therefore the sta­bility of the recursive finite memory filter is guaranteed. The implementations of the nonrecursive and recursive finite memory filters are obtained and the performance of both filters is compared. The performance of the finite memory filter is compared with the one of discrete Kalman filter. It is shown that the finite memory filter requires considerably less computations than the Kalman filter. The Kalman filter showed a better performance in the presence of white Gaussian noise while in the presence of non-gaussian noise both filter performed similarly. A 2-D finite memory filter is developed by a second order bivariant polynomial to a set of 2-D measurements. A weighted least-squares perfor­mance index is used to ensure stability and the performance of the recursive and nonrecursive implementation is compared.
dc.format.extent96 pages
dc.identifier.urihttps://hdl.handle.net/1828/20217
dc.rightsAvailable to the World Wide Weben_US
dc.titleDesign of finite memory filtersen_US
dc.typeThesisen_US

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