Fixed Interval Smoothing for State Space Models
Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, ec...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2001
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| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
609 |
| Schlagworte: | |
| Online-Zugang: | DE-573 DE-634 URL des Erstveröffentlichers |
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| 520 | |a Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. 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. Fixed Interval Smoothing for State Space Models: 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. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics | ||
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| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-1-4615-1691-0 |
| format | Electronic eBook |
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| id | DE-604.BV045148939 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-24T06:50:45Z |
| institution | BVB |
| isbn | 9781461516910 |
| language | English |
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| publishDate | 2001 |
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| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Weinert, Howard L. Verfasser aut Fixed Interval Smoothing for State Space Models by Howard L. Weinert Boston, MA Springer US 2001 1 Online-Ressource (X, 119 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 609 Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. 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. Fixed Interval Smoothing for State Space Models: 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. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics Engineering Electrical Engineering Signal, Image and Speech Processing Statistics, general Statistics Electrical engineering Erscheint auch als Druck-Ausgabe 9781461356806 https://doi.org/10.1007/978-1-4615-1691-0 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Weinert, Howard L. Fixed Interval Smoothing for State Space Models Engineering Electrical Engineering Signal, Image and Speech Processing Statistics, general Statistics Electrical engineering |
| title | Fixed Interval Smoothing for State Space Models |
| title_auth | Fixed Interval Smoothing for State Space Models |
| title_exact_search | Fixed Interval Smoothing for State Space Models |
| title_full | Fixed Interval Smoothing for State Space Models by Howard L. Weinert |
| title_fullStr | Fixed Interval Smoothing for State Space Models by Howard L. Weinert |
| title_full_unstemmed | Fixed Interval Smoothing for State Space Models by Howard L. Weinert |
| title_short | Fixed Interval Smoothing for State Space Models |
| title_sort | fixed interval smoothing for state space models |
| topic | Engineering Electrical Engineering Signal, Image and Speech Processing Statistics, general Statistics Electrical engineering |
| topic_facet | Engineering Electrical Engineering Signal, Image and Speech Processing Statistics, general Statistics Electrical engineering |
| url | https://doi.org/10.1007/978-1-4615-1691-0 |
| work_keys_str_mv | AT weinerthowardl fixedintervalsmoothingforstatespacemodels |