Kalman filtering with real-time applications. Springer series in information sciences. Vol. 17

Chui, Charles K. and Guanrong Chen:

ISBN 10: 3540183957 / ISBN 13: 9783540183952

Editore: Springer Verlag;, 1987

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191 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten wissenschaftlichen Bibliothek und trägt die entsprechenden Kennzeichnungen (Rückenschild, Instituts-Stempel.). Schnitt und Einband sind etwas staubschmutzig; Einbandkanten sind leicht bestossen; der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut.Sprache: englisch. Sprache: Englisch Gewicht in Gramm: 420.

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Dati bibliografici

• Titolo: Kalman filtering with real-time applications. Springer series in information sciences. Vol. 17
• Editore: Springer Verlag
• Data di pubblicazione: 1987
• Lingua: Inglese
• ISBN 10: 3540183957
• ISBN 13: 9783540183952
• Rilegatura: gebundene Ausgabe
• Condizione: Gut
• Cataloghi dei venditori:
  • 2041 Elektrotechnik

Descrizione articolo

Riassunto

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 min­ imum 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 use­ ful 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 prac­ titioners 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 thor­ ough 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 assump­ tions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach.