Nonlinear filters : theory and applications / Peyman Setoodeh, Saeid Habibi, Simon Haykin.
By: Setoodeh, Peyman [author.]
Contributor(s): Habibi, Saeid [author.] | Haykin, Simon S [author.] | John Wiley & Sons [publisher.]
Publisher: Hoboken, NJ : John Wiley & Sons, Inc., 2022Copyright date: �2022Description: 1 online resource (xxii, 273 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781118835814 ; 9781119078159; 1119078156; 9781119078180; 1119078180; 9781119078166; 1119078164Subject(s): Nonlinear control theory | Digital filters (Mathematics) | Signal processing -- Digital techniquesGenre/Form: Electronic books.DDC classification: 629.8/36 LOC classification: QA402.35 | .S48 2022Online resources: Full text is available at Wiley Online Library Click here to view. Summary: "This book fills the gap between the literature on nonlinear filters and nonlinear observers by presenting a new state estimation strategy, the smooth variable structure filter (SVSF). The book is a valuable resource to researchers outside of the control society, where literature on nonlinear observers is less well-known. SVSF is a predictor-corrector estimator that is formulated based on a stability theorem, to confine the estimated states within a neighborhood of their true values. It has the potential to improve performance in the presence of severe and changing modeling uncertainties and noise. An important advantage of the SVSF is the availability of a set of secondary performance indicators that pertain to each estimate. This allows for dynamic refinement of the filter model. The combination of SVSF's robust stability and its secondary indicators of performance make it a powerful estimation tool, capable of compensating for uncertainties that are abruptly introduced in the system"-- Provided by publisher.| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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COLLEGE LIBRARY | COLLEGE LIBRARY | 629.836 Se77 2022 (Browse shelf) | Available |
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Includes bibliographical references and index.
"This book fills the gap between the literature on nonlinear filters and nonlinear observers by presenting a new state estimation strategy, the smooth variable structure filter (SVSF). The book is a valuable resource to researchers outside of the control society, where literature on nonlinear observers is less well-known. SVSF is a predictor-corrector estimator that is formulated based on a stability theorem, to confine the estimated states within a neighborhood of their true values. It has the potential to improve performance in the presence of severe and changing modeling uncertainties and noise. An important advantage of the SVSF is the availability of a set of secondary performance indicators that pertain to each estimate. This allows for dynamic refinement of the filter model. The combination of SVSF's robust stability and its secondary indicators of performance make it a powerful estimation tool, capable of compensating for uncertainties that are abruptly introduced in the system"-- Provided by publisher.
About the Author
Peyman Setoodeh, PhD, is Visiting Professor with the Centre for Mechatronics and Hybrid Technologies (CMHT) at McMaster University. He is a Senior Member of the IEEE.
Saeid Habibi, PhD, is Professor and former Chair of the Department of Mechanical Engineering and the Director of the Centre for Mechatronics and Hybrid Technologies (CMHT) at McMaster University. He is a Fellow of the ASME and the CSME as well as a Canada Research Chair and a Senior NSERC Industrial Research Chair.
Simon Haykin, PhD, is Distinguished University Professor with the Department of Electrical and Computer Engineering and the Director of the Cognitive Systems Laboratory (CSL) at McMaster University. He is a Fellow of the IEEE and the Royal Society of Canada. He is a recipient of the Henry Booker Gold Medal from the International Union of Radio Science, the IEEE James H. Mulligan Jr. Education Medal, and the IEEE Denis J. Picard Medal for Radar Technologies and Applications.
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