A bottom-up approach that enables readers to master and apply the
latest techniques in state estimation
This book offers the best mathematical approaches to estimating the
state of a general system. The author presents state estimation
theory clearly and rigorously, providing the right amount of
advanced material, recent research results, and references to
enable the reader to apply state estimation techniques confidently
across a variety of fields in science and engineering.
While there are other textbooks that treat state estimation, this
one offers special features and a unique perspective and
pedagogical approach that speed learning:
* Straightforward, bottom-up approach begins with basic concepts
and then builds step by step to more advanced topics for a clear
understanding of state estimation
* Simple examples and problems that require only paper and pen to
solve lead to an intuitive understanding of how theory works in
practice
* MATLAB(r)-based source code that corresponds to examples in the
book, available on the author’s Web site, enables readers to
recreate results and experiment with other simulation setups and
parameters
Armed with a solid foundation in the basics, readers are presented
with a careful treatment of advanced topics, including unscented
filtering, high order nonlinear filtering, particle filtering,
constrained state estimation, reduced order filtering, robust
Kalman filtering, and mixed Kalman/H? filtering.
Problems at the end of each chapter include both written exercises
and computer exercises. Written exercises focus on improving the
reader’s understanding of theory and key concepts, whereas computer
exercises help readers apply theory to problems similar to ones
they are likely to encounter in industry. With its expert blend of
theory and practice, coupled with its presentation of recent
research results, Optimal State Estimation is strongly recommended
for undergraduate and graduate-level courses in optimal control and
state estimation theory. It also serves as a reference for
engineers and science professionals across a wide array of
industries.
Sobre o autor
DAN SIMON, Ph D, is an Associate Professor at Cleveland State University. Prior to this appointment, Dr. Simon spent fourteen years working for such firms as Boeing, TRW, and several smaller companies.
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