Kalman Filtering: with Real-Time Applications (Springer Series in Information Sciences (17))

Chui Charles, K. und Guanrong Chen:

ISBN 10: 3540646116 / ISBN 13: 9783540646112

Editore: Springer, 1998

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244 p. New! --- Neu und originalverschweißt! 9783540646112 Sprache: Deutsch Gewicht in Gramm: 381 Softcover: 15.9 x 1.9 x 24.1 cm.

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Dati bibliografici

• Titolo: Kalman Filtering: with Real-Time Applications (Springer Series in Information Sciences (17))
• Editore: Springer
• Data di pubblicazione: 1998
• Lingua: Inglese
• ISBN 10: 3540646116
• ISBN 13: 9783540646112
• Rilegatura: Brossura
• Edizione: 3rd ed.
• Cataloghi dei venditori:
  • Technik

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Riassunto

Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. The last two topics are new additions to this third edition. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowled

Dalla quarta di copertina

Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. The last two topics are new additions to this third edition. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.