Average Reviews:
(More customer reviews)I share the disappointment of a previous commenter regarding the rather poor production quality of the book. Both the format and the typefont are clearly inadequate for a book like this.
This is certainly not a book to introduce oneself to the Kalman filter, in spite of
the title. Rather, the author explores from a very personal perspective some of the inner workings of Kalman filtering, drawing connections with e.g. the Gramm-Schmidt orthogonalization. The seasoned user, with already a good understanding of Kalman filtering, can benefit from reading it, the novice will probably be bewildered.
In a book such as this, going into detail into several algorithms, it would have been desirable some attention to numerical stability questions, which are conspicuously absent.
Overall, I did not felt very rewarded for the effort I put reading this book, yet I think it does offer to the experienced reader some insights which are different --one might say "orthogonal"-- to what is found in other books.
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System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notations, learning just how the Kalman filter works can be a daunting task.
With its mathematically rigorous, “no frills" approach to the basic discrete-time Kalman filter, A Kalman Filter Primer builds a thorough understanding of the inner workings and basic concepts of Kalman filter recursions from first principles. Instead of the typical Bayesian perspective, the author develops the topic via least-squares and classical matrix methods using the Cholesky decomposition to distill the essence of the Kalman filter and reveal the motivations behind the choice of the initializing state vector. He supplies pseudo-code algorithms for the various recursions, enabling code development to implement the filter in practice. The book thoroughly studies the development of modern smoothing algorithms and methods for determining initial states, along with a comprehensive development of the “diffuse" Kalman filter.
Using a tiered presentation that builds on simple discussions to more complex and thorough treatments, A Kalman Filter Primer is the perfect introduction to quickly and effectively using the Kalman filter in practice.
Buy cheap A Kalman Filter Primer (Statistics: A Series of Textbooks and Monographs) now.
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