Estimation, Control, and the Discrete Kalman Filter:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1989
|
| Schriftenreihe: | Applied Mathematical Sciences
71 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 graduate courses given by me in the past decade. Most of these courses were given at the University of Mas sachusetts at Amherst |
| Beschreibung: | 1 Online-Ressource (XIV, 276 p) |
| ISBN: | 9781461245285 9781461288640 |
| ISSN: | 0066-5452 |
| DOI: | 10.1007/978-1-4612-4528-5 |
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| 500 | |a 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 graduate courses given by me in the past decade. Most of these courses were given at the University of Mas sachusetts at Amherst | ||
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Datensatz im Suchindex
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| indexdate | 2024-07-10T01:21:06Z |
| institution | BVB |
| isbn | 9781461245285 9781461288640 |
| issn | 0066-5452 |
| language | English |
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| spelling | Catlin, Donald E. Verfasser aut Estimation, Control, and the Discrete Kalman Filter by Donald E. Catlin New York, NY Springer New York 1989 1 Online-Ressource (XIV, 276 p) txt rdacontent c rdamedia cr rdacarrier Applied Mathematical Sciences 71 0066-5452 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 graduate courses given by me in the past decade. Most of these courses were given at the University of Mas sachusetts at Amherst Statistics Systems theory Mathematical optimization Engineering mathematics Statistics, general Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Statistik Regelungstheorie (DE-588)4122327-5 gnd rswk-swf Schätztheorie (DE-588)4121608-8 gnd rswk-swf Kontrolltheorie (DE-588)4032317-1 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 s Schätztheorie (DE-588)4121608-8 s 1\p DE-604 Kontrolltheorie (DE-588)4032317-1 s 2\p DE-604 Regelungstheorie (DE-588)4122327-5 s 3\p DE-604 https://doi.org/10.1007/978-1-4612-4528-5 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Catlin, Donald E. Estimation, Control, and the Discrete Kalman Filter Statistics Systems theory Mathematical optimization Engineering mathematics Statistics, general Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Statistik Regelungstheorie (DE-588)4122327-5 gnd Schätztheorie (DE-588)4121608-8 gnd Kontrolltheorie (DE-588)4032317-1 gnd Kalman-Filter (DE-588)4130759-8 gnd |
| subject_GND | (DE-588)4122327-5 (DE-588)4121608-8 (DE-588)4032317-1 (DE-588)4130759-8 |
| title | Estimation, Control, and the Discrete Kalman Filter |
| title_auth | Estimation, Control, and the Discrete Kalman Filter |
| title_exact_search | Estimation, Control, and the Discrete Kalman Filter |
| title_full | Estimation, Control, and the Discrete Kalman Filter by Donald E. Catlin |
| title_fullStr | Estimation, Control, and the Discrete Kalman Filter by Donald E. Catlin |
| title_full_unstemmed | Estimation, Control, and the Discrete Kalman Filter by Donald E. Catlin |
| title_short | Estimation, Control, and the Discrete Kalman Filter |
| title_sort | estimation control and the discrete kalman filter |
| topic | Statistics Systems theory Mathematical optimization Engineering mathematics Statistics, general Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Statistik Regelungstheorie (DE-588)4122327-5 gnd Schätztheorie (DE-588)4121608-8 gnd Kontrolltheorie (DE-588)4032317-1 gnd Kalman-Filter (DE-588)4130759-8 gnd |
| topic_facet | Statistics Systems theory Mathematical optimization Engineering mathematics Statistics, general Systems Theory, Control Calculus of Variations and Optimal Control; Optimization Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Statistik Regelungstheorie Schätztheorie Kontrolltheorie Kalman-Filter |
| url | https://doi.org/10.1007/978-1-4612-4528-5 |
| work_keys_str_mv | AT catlindonalde estimationcontrolandthediscretekalmanfilter |