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    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
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Estimation, Control, and the Discrete Kalman Filter

Estimation, Control, and the Discrete Kalman Filter

Catlin, Donald E.

Paperback

Series: Applied Mathematical Sciences, Book 71

Technology & EngineeringGeneral Mathematics

ISBN10: 1461288649
ISBN13: 9781461288640
Publisher: Springer Nature
Published: Sep 26 2011
Pages: 276
Weight: 0.93
Height: 0.62 Width: 6.14 Depth: 9.21
Language: English
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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