lectures discrete time filtering de bucy (14 résultats)

Hardcover. Etat : Very Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or limited writing/highlighting. We ship orders daily and Customer Service is our top priority!

Hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Etat : Fine. *Price HAS BEEN REDUCED by 10% until Monday, May 5 (weekend SALE item)* 156 pp., Hardcover, top edge lightly foxed, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Etat : New.

Etat : New. In.

Paperback. Etat : New.

gebundene Ausgabe. Zust: Gutes Exemplar. XV, 156 Seiten, Englisch 474g.

Etat : gebraucht; wie neu.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The theory of linear discrete time filtering started with a paper by Kol mogorov in 1941. He addressed the problem for stationary random se quences and introduced the idea of the innovations process, which is a useful tool for the more general problems considered here. The reader may object and note that Gauss discovered least squares much earlier; however, I want to distinguish between the problem of parameter estimation, the Gauss problem, and that of Kolmogorov estimation of a process. This sep aration is of more than academic interest as the least squares problem leads to the normal equations, which are numerically ill conditioned, while the process estimation problem in the linear case with appropriate assumptions leads to uniformly asymptotically stable equations for the estimator and the gain. The conditions relate to controlability and observability and will be detailed in this volume. In the present volume, we present a series of lectures on linear and nonlinear sequential filtering theory. The theory is due to Kalman for the linear colored observation noise problem; in the case of white observation noise it is the analog of the continuous-time Kalman-Bucy theory. The discrete time filtering theory requires only modest mathematical tools in counterpoint to the continuous time theory and is aimed at a senior-level undergraduate course. The present book, organized by lectures, is actually based on a course that meets once a week for three hours, with each meeting constituting a lecture.

Etat : Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. Außen: vergilbt, angestoßen, Klebereste / Klebespuren. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. | Sprache: Englisch | Produktart: Bücher.

Etat : New.

hardcover. Etat : fine. no jacket. 6 1/4 x 9 1/2 " 156 pages. written with the assistance of B. G. Williams. C. S. Burrus Consulting Editor. part of the Signal Processing and Digital Filtering series. "It discusses linear and nonlinear sequential filtering theory, that is, the problem of estimating a stochastic signal process indirectly observed through a sensor corrupted by a stochastic noise process.".

Paperback. Etat : Like New. Like New. book.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The theory of linear discrete time filtering started with a paper by Kol mogorov in 1941. He addressed the problem for stationary random se quences and introduced the idea of the innovations process, which is a useful tool for the more general problems considered here. The reader may object and note that Gauss discovered least squares much earlier; however, I want to distinguish between the problem of parameter estimation, the Gauss problem, and that of Kolmogorov estimation of a process. This sep aration is of more than academic interest as the least squares problem leads to the normal equations, which are numerically ill conditioned, while the process estimation problem in the linear case with appropriate assumptions leads to uniformly asymptotically stable equations for the estimator and the gain. The conditions relate to controlability and observability and will be detailed in this volume. In the present volume, we present a series of lectures on linear and nonlinear sequential filtering theory. The theory is due to Kalman for the linear colored observation noise problem; in the case of white observation noise it is the analog of the continuous-time Kalman-Bucy theory. The discrete time filtering theory requires only modest mathematical tools in counterpoint to the continuous time theory and is aimed at a senior-level undergraduate course. The present book, organized by lectures, is actually based on a course that meets once a week for three hours, with each meeting constituting a lecture. 176 pp. Englisch.