Morrison N. Tracking Filter Engineering: The Gauss-Newton and Polynomial Filters

New York: Institution of Engineering and Technology, 2013. — 598 p.Identifying an alternative approach to filter engineering and the traditional Kalman filters, this new book highlights the important advantages of the Gauss-Newton filters. 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. 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.Why This Book?
OrganisationBackground
Readme_First
Models, Differential Equations and Transition Matrices
Observation Schemes
Random Vectors and Covariance Matrices - Theory
Random Vectors and Covariance Matrices in Filter Engineering
Bias Errors
Three Tests for ECM ConsistencyNon-Recursive Filtering
Minimum Variance and the Gauss-Aitken Filters
Minimum Variance and the Gauss-Newton Filters
The Master Control Algorithms and Goodness-of-FitRecursive Filtering
The Kalman and Swerling Filters
Polynomial Filtering - 1
Polynomial Filtering - 2