Fundamentals of Kalman filtering : a practical approach
著者
書誌事項
Fundamentals of Kalman filtering : a practical approach
（Progress in astronautics and aeronautics, v. 246）
American Institute of Aeronautics and Astronautics, c2015
4th ed
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
In 2008 the National Academy of Engineering awarded Rudolf Kalman the Charles Stark Draper Prize--the engineering equivalent of the Nobel Prize -- for the development and dissemination of the optimal digital technique (known as the Kalman Filter) that is pervasively used to control a vast array of consumer, health, commercial, and defense products. Fundamentals of Kalman Filtering, Fourth Edition is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. For this edition, source code listings appearing in the text have been converted from FORTRAN to MATLAB(R). In addition, both FORTRAN and MATLAB* source code are available electronically for all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce mistakes to the initial filter designs to show the reader what happens when the filter is not working properly.The text carefully sets up a problem before the Kalman filter is actually formulated, to give the reader an intuitive feel for the problem being addressed. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach. The fourth edition features four new chapters presenting the following techniques: the State Dependent Ricatti Equation Filter (SDRE), the Unscented Kalman Filter (UKF), the Interactive Multiple Model (IMM) Filter Bank, and the Cramer-Rao lower bound (CRLB), for finding the best that a filter can perform.
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