Estimation, Control, and the Discrete Kalman Filter by Donald E. Catlin
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Estimation, Control, and the Discrete Kalman Filter

Estimation, Control, and the Discrete Kalman Filter


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About the Book

In 1960, R. E. Kalman published his celebrated paper on recursive min- imum variance estimation in dynamical systems [14]. This paper, which introduced an algorithm that has since been known as the discrete Kalman filter, produced a virtual revolution in the field of systems engineering. Today, Kalman filters are used in such diverse areas as navigation, guid- ance, oil drilling, water and air quality, and geodetic surveys. In addition, Kalman's work led to a multitude of books and papers on minimum vari- ance estimation in dynamical systems, including one by Kalman and Bucy on continuous time systems [15]. Most of this work was done outside of the mathematics and statistics communities and, in the spirit of true academic parochialism, was, with a few notable exceptions, ignored by them. This text is my effort toward closing that chasm. For mathematics students, the Kalman filtering theorem is a beautiful illustration of functional analysis in action; Hilbert spaces being used to solve an extremely important problem in applied mathematics. For statistics students, the Kalman filter is a vivid example of Bayesian statistics in action. The present text grew out of a series of graduate courses given by me in the past decade. Most of these courses were given at the University of Mas- sachusetts at Amherst.


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Product Details
  • ISBN-13: 9781461288640
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Edition: Softcover reprint of the original 1st ed. 1989
  • Language: English
  • Returnable: Y
  • Spine Width: 16 mm
  • Width: 156 mm
  • ISBN-10: 1461288649
  • Publisher Date: 26 Sep 2011
  • Binding: Paperback
  • Height: 234 mm
  • No of Pages: 276
  • Series Title: Applied Mathematical Sciences
  • Weight: 471 gr


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