Factorization Methods for Discrete Sequential Estimation (Paperback)
Language: English
Published by Dover Publications Inc., New York, 2006
- Softcover
- New

Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. This estimation reference text thoroughly describes matrix factorization methods successfully employed by numerical analysts, familiarizing readers with the techniques that lead to efficient, economical, reliable, and flexible estimation algorithms.Topics include a review of least squares data processing and the Kalman filter algorithm; positive definite matrices, the Cholesky decomposition, and some of their applications; Householder orthogonal transformations; sequential square root data processing; mapping effects and process noise; biases and correlated process noise; and covariance analysis of effects due to mismodeled variables and incorrect filter a priori statistics. The concluding chapters explore SRIF error analysis of effects due to mismodeled variables and incorrect filter a priori statistics as well as square root information smoothing. Geared toward advanced undergraduates and graduate students, this pragmatically oriented and detailed presentation is also a useful reference, featuring numerous helpful appendixes throughout the text. Geared toward advanced undergraduates and graduate students, this text describes matrix factorization methods employed by numerical analysts, featuring techniques that lead to efficient, economical, reliable, and flexible estimation algorithms. 1977 edition. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9780486449814
- Title
- Factorization Methods for Discrete Sequential Estimation (Paperback)
- Author
- Gerald J. Bierman
- Publisher
- Dover Publications Inc., New York
- Publication year
- 2006
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0486449815
- ISBN 13
- 9780486449814
Topics include a review of least squares data processing and the Kalman filter algorithm; positive definite matrices, the Cholesky decomposition, and some of their applications; Householder orthogonal transformations; sequential square root data processing; mapping effects and process noise; biases and correlated process noise; and covariance analysis of effects due to mismodeled variables and incorrect filter a priori statistics. The concluding chapters explore SRIF error analysis of effects due to mismodeled variables and incorrect filter a priori statistics as well as square root information smoothing. Geared toward advanced undergraduates and graduate students, this pragmatically oriented and detailed presentation is also a useful reference, featuring numerous helpful appendixes throughout the text.
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CitiRetail
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