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OverviewThis 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. Full Product DetailsAuthor: Norman MorrisonPublisher: Institution of Engineering and Technology Imprint: Institution of Engineering and Technology Dimensions: Width: 15.60cm , Height: 3.60cm , Length: 23.40cm Weight: 0.057kg ISBN: 9781849195546ISBN 10: 1849195544 Pages: 576 Publication Date: 15 December 2012 Audience: College/higher education , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsPart 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 - 2ReviewsAuthor InformationNorman 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. Tab Content 6Author Website:Countries AvailableAll regions |