Buch, Englisch, Band 609, 119 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 213 g
Reihe: The Springer International Series in Engineering and Computer Science
Buch, Englisch, Band 609, 119 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 213 g
Reihe: The Springer International Series in Engineering and Computer Science
ISBN: 978-1-4613-5680-6
Verlag: Springer US
This monograph addresses problems for which a linear stochastic state space model is available, in which case the objective is to compute the linear least-squares estimate of the state vector in a fixed interval, using observations previously collected in that interval. The author uses a geometric approach based on the method of complementary models. Using the simplest possible notation, he presents straightforward derivations of the four types of fixed-interval smoothing algorithms, and compares the algorithms in terms of efficiency and applicability. Results show that the best algorithm has received the least attention in the literature.
- includes new material on interpolation, fast square root implementations, and boundary value models;
- is the first book devoted to smoothing;
- contains an annotated bibliography of smoothing literature;
- uses simple notation and clear derivations;
- compares algorithms from a computational perspective;
- identifies a best algorithm.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
Weitere Infos & Material
Ch. 1 Introduction.- 1.1 State Space Models.- 1.2 Fixed Interval Smoothing.- 1.3 Notes and References.- Ch. 2 Complementary Models.- 2.1 Discrete Case.- 2.2 Continuous Case.- 2.3 Notes and References.- Ch. 3 Discrete Smoothers.- 3.1 Backward-Forward Smoother.- 3.2 Forward-Backward Smoothers.- 3.3 Two-Filter Smoother.- 3.4 Square Root Implementations.- 3.5 Interpolated Case.- 3.6 Notes and References.- Ch. 4 Continuous Smoothers.- 4.1 Backward-Forward Smoother.- 4.2 Forward-Backward Smoothers.- 4.3 Two-Filter Smoother.- 4.4 Notes and References.- Ch. 5 Boundary Value Models.- 5.1 Complementary Model.- 5.2 Backward-Forward Smoother.- 5.3 Notes and References.- Annotated Bibliography.- Author Index.