Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation
(eBook)

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Published
Dover Publications, 2018.
Format
eBook
Status
Available Online

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Language
English
ISBN
9780486835549

Syndetics Unbound

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APA Citation, 7th Edition (style guide)

S. M. Bozic., & S. M. Bozic|AUTHOR. (2018). Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation . Dover Publications.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

S. M. Bozic and S. M. Bozic|AUTHOR. 2018. Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation. Dover Publications.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

S. M. Bozic and S. M. Bozic|AUTHOR. Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation Dover Publications, 2018.

MLA Citation, 9th Edition (style guide)

S. M. Bozic, and S. M. Bozic|AUTHOR. Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation Dover Publications, 2018.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID5f2e0a89-0524-cae7-da6d-abf28423ce34-eng
Full titledigital and kalman filtering an introduction to discrete time filtering and optimum linear estimation
Authorbozic s m
Grouping Categorybook
Last Update2024-05-15 02:00:45AM
Last Indexed2024-05-16 03:12:05AM

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First LoadedApr 4, 2023
Last UsedMay 12, 2024

Hoopla Extract Information

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    [synopsis] => This text for advanced undergraduates and graduate students provides a concise introduction to increasingly important topics in electrical engineering: digital filtering, filter design, and applications in the form of the Kalman and Wiener filters. The first half focuses on digital filtering, covering FIR and IIR filter design and other concepts. The second half addresses filtering noisy data to extract a signal, with chapters on non-recursive (FIR Wiener) estimation, recursive (Kalman) estimation, and optimum estimation of vector signals. The treatment is presented in tutorial form, but readers are assumed to be familiar with basic circuit theory, statistical averages, and elementary matrices. Central topics are developed gradually, including both worked examples and problems with solutions, and this second edition features new material and problems.
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