A Kalman filter primer / R.L. Eubank.
Eubank (mathematics and statistics, Arizona State U.) offers a self-contained, concise rigorous derivation of all the basic Kalman filter recursions from first principles. He lays out the basic prediction problem for signal-plus-noise models, deriving the Gramm-Schmidt algorithm and Cholesky decompo...
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| Online Access: |
Full Text (via Taylor & Francis) |
|---|---|
| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, Fla. :
Chapman & Hall/CRC,
2006.
|
| Series: | Statistics, textbooks and monographs ;
v. 186. |
| Subjects: |
MARC
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| 100 | 1 | |a Eubank, R. L. |q (Randy L.) | |
| 245 | 1 | 2 | |a A Kalman filter primer / |c R.L. Eubank. |
| 260 | |a Boca Raton, Fla. : |b Chapman & Hall/CRC, |c 2006. | ||
| 300 | |a 1 online resource (186 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 347 | |a data file. | ||
| 490 | 1 | |a Statistics, textbooks and monographs ; |v v. 186. | |
| 504 | |a Includes bibliographical references (pages 183-184) and index. | ||
| 505 | 0 | |a Chapter 1 Signal-Plus-Noise Models -- chapter 2 The Fundamental Covariance Structure -- chapter 3 Recursions for L and L−1 -- chapter 4 Forward Recursions -- chapter 5 Smoothing -- chapter 6 Initialization -- chapter 7 Normal Priors -- chapter 8 A General State-Space Model. | |
| 520 | |a Eubank (mathematics and statistics, Arizona State U.) offers a self-contained, concise rigorous derivation of all the basic Kalman filter recursions from first principles. He lays out the basic prediction problem for signal-plus-noise models, deriving the Gramm-Schmidt algorithm and Cholesky decomposition. He covers the fundamental covariance struc. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Kalman filtering |v Textbooks. | |
| 650 | 7 | |a Kalman filtering. |2 fast |0 (OCoLC)fst00985838. | |
| 655 | 7 | |a Textbooks. |2 fast |0 (OCoLC)fst01423863. | |
| 776 | 0 | 8 | |i Print version: |a Eubank, R.L. (Randy L.). |t Kalman filter primer. |d Boca Raton, Fla. : Chapman & Hall/CRC, 2006 |z 0824723651 |w (DLC) 2005051951 |w (OCoLC)61253979. |
| 830 | 0 | |a Statistics, textbooks and monographs ; |v v. 186. | |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://www.taylorfrancis.com/books/9780429117596 |z Full Text (via Taylor & Francis) |
| 880 | 0 | |6 505-00/(S |a Signal-Plus-Noise Models ; Introduction; The Prediction Problem; State-Space Models; What Lies Ahead; The Fundamental Covariance Structure ; Introduction; Some Tools of the Trade; State and Innovation Covariances; An Example; Recursions for L and L −1 ; Introduction; Recursions for L ; Recursions for L −1 ; An Example; Forward Recursions ; Introduction; Computing the Innovations; State and Signal Prediction; Other Options; Examples; Smoothing ; Introduction; Fixed Interval Smoothing; Examples; Initialization ; Introduction; Diffuseness; Diffuseness and Least-Squares Estimation; An Example; Normal Priors ; Introduction; Likelihood Evaluation; Diffuseness; Parameter Estimation; An Example; A General State-Space Model ; Introduction; KF Recursions; Estimation of β ; Likelihood Evaluation; Appendix A: The Cholesky Decomposition ; Appendix B: Notation Guide ; References ; Index. | |
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