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This book provides an introductory, yet comprehensive, treatment of both Wiener and Kalman filtering, along with a development of least-squares estimation, maximum likelihood estimation, and maximum a posteriori estimation based on discrete-time measurements. A good deal of emphasis is placed in the text on showing how these different approaches to estimation fit together to form a systematic development of optimal estimation. Included in the text is a chapter on nonlinear filtering, focusing on the extended Kalman filter (EKF) and a new measurement update that uses the Levenburg-Marquardt algorithm to obtain more accurate results in comparison to the EKF measurement update. Applications of nonlinear filtering are also considered, including the identification of nonlinear systems modeled by neural networks, FM demodulation, target tracking based on polar-coordinate measurements, and multiple target tracking.
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Previews available in: English
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Edition | Availability |
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1
Introduction to Optimal Estimation (Advanced Textbooks in Control and Signal Processing)
October 29, 1999, Springer
Paperback
in English
- 1 edition
185233133X 9781852331337
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Feedback?July 1, 2019 | Edited by MARC Bot | import existing book |
November 11, 2012 | Edited by 67.233.4.61 | Edited without comment. |
June 20, 2010 | Edited by 76.1.164.241 | Edited without comment. |
April 28, 2010 | Edited by Open Library Bot | Linked existing covers to the work. |
March 17, 2010 | Created by WorkBot | work found |