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Approximate Kalman Filtering
By Guanrong Chen (editor)
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Bibliographic Detail
Publisher World Scientific Pub Co Inc
Publication date November 1, 1993
Binding Hardcover
Book category Adult Non-Fiction
ISBN-13 9789810213596
ISBN-10 981021359X
Dimensions 0.75 by 6.50 by 9 in.
Weight 1.20 lbs.
Availability§ Publisher Out of Stock
Published in Other
Original list price $129.00
Other format details sci/tech
§As reported by publisher
Summaries and Reviews
Amazon.com description: Product Description: Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence “approximate Kalman filtering” becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.

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Hardcover
Book cover for 9789810213596
 
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from World Scientific Pub Co Inc (November 1, 1993)
9789810213596 | details & prices | 6.50 × 9.00 × 0.75 in. | 1.20 lbs | List price $129.00
About: Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.

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