Norman Morrison

Tracking Filter Engineering

The Gauss-Newton and Polynomial Filters

• Gebundenes Buch

Produktbeschreibung

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."

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This book provides a complete discussion of the Gauss-Newton filters, including all necessary theoretical background. This book also covers the expanding and fading memory polynomial filters based on the Legendre and Laguerre orthogonal polynomials, and how these can serve as pre-filters for Gauss-Newton. Of particular interest is a new approach to the tracking of manoeuvring targets that the Gauss-Newton filters make possible. Fourteen carefully constructed computer programs demonstrate the use and power of Gauss-Newton and the polynomial filters. Two of these also include Kalman and Swerling filters in addition to Gauss-Newton, all three of which process identical data that have been pre-filtered by polynomial filters. These two programs demonstrate Kalman and Swerling instability, to which Gauss-Newton is immune, and also the fact that if an attempt is made to forestall Kalman/Swerling instability by the use of a Q matrix, then they cease to be Cramér-Rao consistent and become less accurate than the always Cramér-Rao consistent Gauss-Newton filters.

Produktdetails

• Verlag: Institution of Engineering & Technology
• Seitenzahl: 576
• Erscheinungstermin: 20. Dezember 2012
• Englisch
• Abmessung: 240mm x 161mm x 35mm
• Gewicht: 1026g
• ISBN-13: 9781849195546
• ISBN-10: 1849195544
• Artikelnr.: 36804734

Autorenporträt

Norman Morrison was born in Dewetsdorp, South Africa. He received a dual-discipline PhD degree in Electrical Engineering and Applied Mathematics from Case Western Reserve University, Cleveland, Ohio in 1963, after which he worked at Bell Labs Ballistic Missile Defence in Whippany, New Jersey. He later served as Chief Scientist for the Harris Corporation in Cleveland, and in his last post in the USA, before returning to South Africa, he was Vice-President Technology at Knight-Ridder Communications in Miami, Florida. In South Africa he taught applied mathematics at the University of Cape Town for twenty years. He is currently employed by the South African Department of Defence, working on the development of advanced radar systems.

Inhaltsangabe

Part 1: Background
Chapter 1: Readme_First
Chapter 2: Models, differential equations and transition matrices
Chapter 3: Observation schemes
Chapter 4: Random vectors and covariance matrices - theory
Chapter 5: Random vectors and covariance matrices in filter engineering
Chapter 6: Bias errors
Chapter 7: Three tests for ECM consistency
Part 2: Non-recursive filtering
Chapter 8: Minimum variance and the Gauss-Aitken filters
Chapter 9: Minimum variance and the Gauss-Newton filters
Chapter 10: The master control algorithms and goodness-of-fit
Part 3: Recursive Filtering
Chapter 11: The Kalman and Swerling filters
Chapter 12: Polynomial filtering - 1
Chapter 13: Polynomial filtering - 2