"Intuitive understanding of Kalman filtering with MATLAB" / Armando Barreto, Electrical & Computer Engineering Department, Florida International University, Malek Adjouadi, Electrical & Computer Engineering Department, Florida International University, Francisco R. Ortega, Department of Computer Science, Colorado State University, Nonnarit O-larnnithipong, Electrical & Computer Engineering Department, Florida International University.

By:

Barreto, Armando, 1963- [author.]

Contributor(s):

Material type: Text

TextPublisher: Boca Raton, FL : CRC Press, 2020Edition: First editionDescription: pages cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780367191351
9780367191337

Subject(s):

DDC classification:

005 23

LOC classification:

QA402.3 .B3535 2020

Summary: "The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm"-- Provided by publisher.

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Books	RLKU Library & Information Resource Center	005 BAR (Browse shelf(Opens below))	Available	13071
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Includes bibliographical references and index.

"The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm"-- Provided by publisher.

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