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Titlebook: Estimation, Control, and the Discrete Kalman Filter; Donald E. Catlin Book 1989 Sringer-Verlag New York Inc. 1989 Bias.Computer-Aided Desi

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书目名称Estimation, Control, and the Discrete Kalman Filter
编辑Donald E. Catlin
视频video
丛书名称Applied Mathematical Sciences
图书封面Titlebook: Estimation, Control, and the Discrete Kalman Filter;  Donald E. Catlin Book 1989 Sringer-Verlag New York Inc. 1989 Bias.Computer-Aided Desi
描述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 graduat
出版日期Book 1989
关键词Bias; Computer-Aided Design (CAD); Estimator; Normal; Operator; Paro; Tracking; bayesian statistics; best fi
版次1
doihttps://doi.org/10.1007/978-1-4612-4528-5
isbn_softcover978-1-4612-8864-0
isbn_ebook978-1-4612-4528-5Series ISSN 0066-5452 Series E-ISSN 2196-968X
issn_series 0066-5452
copyrightSringer-Verlag New York Inc. 1989
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Tami Blumenfield,Helaine Silverman a way that the resulting distribution contains no more information than is inherent in the data. The first attempt to do this was by Laplace and was called the “Principle of Insufficient Reason.” This principle said that two events should be assigned the same probability if there is no reason to do
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https://doi.org/10.1007/978-3-319-78789-3. Moreover, we also calculated an expression for the so-called covariance of the estimation error. Specifically, then, our output was a random variable . representing an estimator of ., and a matrix . defined by . Both of these outputs were based on knowledge of the means μ. and μ., the covariances
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Camilla Casonato,Bertrando BonfantiniWe begin by introducing the notion of a probability measure, and then we will discuss the intuitive interpretation of this definition.
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