Kalman Filtering with Real-Time Applications:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1987
|
| Schriftenreihe: | Springer Series in Information Sciences
17 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and minimum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fue control. With the recent development of high-speed computers, the Kalman filter has become more useful even for very complicated real-time applications. lnspite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most practitioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thorough discussion of its mathematical theory and applications to various elementary real-time problems. A very elementary derivation of the filtering equations is fust presented. By assuming that certain matrices are nonsingular, the advantage of this approach is that the optimality of the Kalman filter can be easily understood. Of course these assumptions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach |
| Beschreibung: | 1 Online-Ressource (XV, 191 p) |
| ISBN: | 9783662025086 9783662025109 |
| ISSN: | 0720-678X |
| DOI: | 10.1007/978-3-662-02508-6 |
Internformat
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| 264 | 1 | |a Berlin, Heidelberg |b Springer Berlin Heidelberg |c 1987 | |
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| 500 | |a Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and minimum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fue control. With the recent development of high-speed computers, the Kalman filter has become more useful even for very complicated real-time applications. lnspite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most practitioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thorough discussion of its mathematical theory and applications to various elementary real-time problems. A very elementary derivation of the filtering equations is fust presented. By assuming that certain matrices are nonsingular, the advantage of this approach is that the optimality of the Kalman filter can be easily understood. Of course these assumptions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach | ||
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Datensatz im Suchindex
| _version_ | 1804153079457644544 |
|---|---|
| any_adam_object | |
| author | Chui, Charles K. |
| author_facet | Chui, Charles K. |
| author_role | aut |
| author_sort | Chui, Charles K. |
| author_variant | c k c ck ckc |
| building | Verbundindex |
| bvnumber | BV042414161 |
| classification_tum | PHY 000 |
| collection | ZDB-2-PHA ZDB-2-BAE |
| ctrlnum | (OCoLC)863930540 (DE-599)BVBBV042414161 |
| dewey-full | 621.36 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.36 |
| dewey-search | 621.36 |
| dewey-sort | 3621.36 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Physik Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-3-662-02508-6 |
| format | Electronic eBook |
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| id | DE-604.BV042414161 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:20:54Z |
| institution | BVB |
| isbn | 9783662025086 9783662025109 |
| issn | 0720-678X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027849654 |
| oclc_num | 863930540 |
| open_access_boolean | |
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| owner_facet | DE-91 DE-BY-TUM DE-83 |
| physical | 1 Online-Ressource (XV, 191 p) |
| psigel | ZDB-2-PHA ZDB-2-BAE ZDB-2-PHA_Archive |
| publishDate | 1987 |
| publishDateSearch | 1987 |
| publishDateSort | 1987 |
| publisher | Springer Berlin Heidelberg |
| record_format | marc |
| series | Springer Series in Information Sciences |
| series2 | Springer Series in Information Sciences |
| spelling | Chui, Charles K. Verfasser aut Kalman Filtering with Real-Time Applications by Charles K. Chui, Guanrong Chen Berlin, Heidelberg Springer Berlin Heidelberg 1987 1 Online-Ressource (XV, 191 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Information Sciences 17 0720-678X Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and minimum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fue control. With the recent development of high-speed computers, the Kalman filter has become more useful even for very complicated real-time applications. lnspite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most practitioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thorough discussion of its mathematical theory and applications to various elementary real-time problems. A very elementary derivation of the filtering equations is fust presented. By assuming that certain matrices are nonsingular, the advantage of this approach is that the optimality of the Kalman filter can be easily understood. Of course these assumptions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach Physics Mathematical physics Optics, Optoelectronics, Plasmonics and Optical Devices Mathematical Methods in Physics Numerical and Computational Physics Mathematische Physik Echtzeitsystem (DE-588)4131397-5 gnd rswk-swf Mathematische Methode (DE-588)4155620-3 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 s Echtzeitsystem (DE-588)4131397-5 s 1\p DE-604 Mathematische Methode (DE-588)4155620-3 s 2\p DE-604 Chen, Guanrong Sonstige oth Springer Series in Information Sciences 17 (DE-604)BV000008063 17 https://doi.org/10.1007/978-3-662-02508-6 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 |
| spellingShingle | Chui, Charles K. Kalman Filtering with Real-Time Applications Springer Series in Information Sciences Physics Mathematical physics Optics, Optoelectronics, Plasmonics and Optical Devices Mathematical Methods in Physics Numerical and Computational Physics Mathematische Physik Echtzeitsystem (DE-588)4131397-5 gnd Mathematische Methode (DE-588)4155620-3 gnd Kalman-Filter (DE-588)4130759-8 gnd |
| subject_GND | (DE-588)4131397-5 (DE-588)4155620-3 (DE-588)4130759-8 |
| title | Kalman Filtering with Real-Time Applications |
| title_auth | Kalman Filtering with Real-Time Applications |
| title_exact_search | Kalman Filtering with Real-Time Applications |
| title_full | Kalman Filtering with Real-Time Applications by Charles K. Chui, Guanrong Chen |
| title_fullStr | Kalman Filtering with Real-Time Applications by Charles K. Chui, Guanrong Chen |
| title_full_unstemmed | Kalman Filtering with Real-Time Applications by Charles K. Chui, Guanrong Chen |
| title_short | Kalman Filtering with Real-Time Applications |
| title_sort | kalman filtering with real time applications |
| topic | Physics Mathematical physics Optics, Optoelectronics, Plasmonics and Optical Devices Mathematical Methods in Physics Numerical and Computational Physics Mathematische Physik Echtzeitsystem (DE-588)4131397-5 gnd Mathematische Methode (DE-588)4155620-3 gnd Kalman-Filter (DE-588)4130759-8 gnd |
| topic_facet | Physics Mathematical physics Optics, Optoelectronics, Plasmonics and Optical Devices Mathematical Methods in Physics Numerical and Computational Physics Mathematische Physik Echtzeitsystem Mathematische Methode Kalman-Filter |
| url | https://doi.org/10.1007/978-3-662-02508-6 |
| volume_link | (DE-604)BV000008063 |
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