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Rating: Summary: Excellent book on estimation/Kalman filter Review: I don't usually write online reviews but this book is so clear and useful that I really want to recommend it to others. It is well written with a good outline and summary for every chapter. It also has a pretty diverse range of topics on estimation, including an introductory chapter on basic estimation approaches (e.g., ML, MAP, least squares), and very practical extensions (e.g., state augmentation, square-root filters). Even though I am not in EE and some of the examples are thus not particularly helpful to me, I still find this book one of the best of all the estimation/Kalman filter books out there.
Rating: Summary: best standard book for target tracking system Review: I think any person who major in target tracking system related to the Kalman filter must see this book. This book present the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for target tracking. The book covers the basic concepts and estimation techniques for static and dynamic systems, linear and nonlinear, as well as adaptive estiomation. This constitutes a one semester graduate course in estimation theory in an electrical/systems engineering program. The discussion deals mainly with discrete time estimation algorithms, which are natural for digital computer implementation. The basic state estimation algorithm-the Kalman filter-is presented in discrete as well as in continuous time. The use of the estimation algorithms is illustrated on kinematic motion models because they reveal all the major issues and in particular the subtleties encountered in estimation, and this serves as an introdution to tracking. Guidelines for tracking filter design-selection of the filter design parameters-are given and illustrated in several examples. At the end of each chapter, a number of problems that enhance the understanding of the theory and the connection of the theoretical material to the real world are given. And I have this book as text for my paper.
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