Tracking Filter Engineering
商品資訊
系列名:Iet Radar, Sonar and Navigation
ISBN13:9781849195546
出版社:Inst of Engineering & Technology
作者:Norman Morrison
出版日:2013/04/30
裝訂/頁數:精裝/320頁
規格:23.5cm*15.9cm*3.3cm (高/寬/厚)
定價
:NT$ 9675 元若需訂購本書,請電洽客服 02-25006600[分機130、131]。
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The book provides a complete theoretical background, and then discusses in detail the Gauss-Newton filters. Of particular interest is a new approach to the tracking of maneuvering targets that is made possible by these filters. The book also covers the expanding and fading memory polynomial filters based on the Legendre and Laguerre orthogonal polynomials, and how these can be used in conjunction with Gauss-Newton.
Fourteen carefully constructed computer programs cover the theoretical background, and also demonstrate the power of the Gauss-Newton and polynomial filters. Two of these programs include Kalman, Swerling and Gauss-Newton filters, all three processing identical data. These demonstrate Kalman and Swerling instability to which Gauss-Newton is immune, and also the fact that if an attempt is made to forestall Kalman and Swerling instability by the use of a Q matrix, then they are no longer Cramer-Rao consistent and become noticeably less accurate than the always Cramer-Rao consistent Gauss-Newton filters.
This book will be of interest to filter engineering practitioners, to graduate-level newcomers wishing to learn about Gauss-Newton and polynomial filters and to university lecturers who might wish to include material on the Gauss-Newton and polynomial filters in graduate-level courses on tracking filter engineering.
Fourteen carefully constructed computer programs cover the theoretical background, and also demonstrate the power of the Gauss-Newton and polynomial filters. Two of these programs include Kalman, Swerling and Gauss-Newton filters, all three processing identical data. These demonstrate Kalman and Swerling instability to which Gauss-Newton is immune, and also the fact that if an attempt is made to forestall Kalman and Swerling instability by the use of a Q matrix, then they are no longer Cramer-Rao consistent and become noticeably less accurate than the always Cramer-Rao consistent Gauss-Newton filters.
This book will be of interest to filter engineering practitioners, to graduate-level newcomers wishing to learn about Gauss-Newton and polynomial filters and to university lecturers who might wish to include material on the Gauss-Newton and polynomial filters in graduate-level courses on tracking filter engineering.
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