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Kalman Filter for Beginners : with MATLAB Examples

Author: Pʻir-yŏng Kim; Lynn Huh; A-JIN Publishing,
Publisher: [United States] : [CreateSpace], ©2011.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Dwarfs your fear towards complicated mathematical derivations and proofs. Experience Kalman filter with hands-on examples to grasp the essence. A book long awaited by anyone who could not dare to put their first step into Kalman filter. The author presents Kalman filter and other useful filters without complicated mathematical derivation and proof but with hands-on examples in MATLAB that will guide you  Read more...
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Document Type: Book
All Authors / Contributors: Pʻir-yŏng Kim; Lynn Huh; A-JIN Publishing,
ISBN: 9781463648350 1463648359
OCLC Number: 757801012
Language Note: Translated from the Korean.
Notes: Publisher's information from Amazon.com.
Originally published in Republic of Korea by A-JIN Publishing in 2010.
Description: xiv, 232 pages : illustrations ; 25 cm
Contents: Recursive filter. Average filter ; Moving average filter ; Low-pass filter ; Summary of Part I --
Theory of Kalman filter. Introduction to Kalman filter ; Estimation process ; Prediction process ; System model ; Summary of Part II --
Examples. Extremely simple example ; Estimating velocity from position ; Tracking an object in an image ; Attitude reference system --
Nonlinear Kalman filter. Extended Kalman filter ; Unscented Kalman filter --
Frequency analysis and filter. High-pass filter ; Complementary filter.
Responsibility: Phil Kim ; Translated by Lynn Huh.

Abstract:

Dwarfs your fear towards complicated mathematical derivations and proofs. Experience Kalman filter with hands-on examples to grasp the essence. A book long awaited by anyone who could not dare to put their first step into Kalman filter. The author presents Kalman filter and other useful filters without complicated mathematical derivation and proof but with hands-on examples in MATLAB that will guide you step-by-step. The book starts with recursive filter and basics of Kalman filter, and gradually expands to application for nonlinear systems through extended and unscented Kalman filters. Also, some topics on frequency analysis including complementary filter are covered. Each chapter is balanced with theoretical background for absolute beginners and practical MATLAB examples to experience the principles explained. Once grabbing the book, you will notice it is not fearful but even enjoyable to learn Kalman filter.

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