product iamge

Tracking filter engineering : the Gauss-Newton and polynominal filters

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

Author(s): Morrison, Norman
Series: IET radar sonar and navigation series 23
Publisher: Institution of Engineering and Technology IET
Year: 2013

Language: English
City: London
Tags: Fi

Content: Part 1: BackgroundChapter 1: Readme_FirstChapter 2: Models, differential equations and transition matricesChapter 3: Observation schemesChapter 4: Random vectors and covariance matrices - theoryChapter 5: Random vectors and covariance matrices in filter engineeringChapter 6: Bias errorsChapter 7: Three tests for ECM consistencyPart 2: Non-recursive filteringChapter 8: Minimum variance and the Gauss-Aitken filtersChapter 9: Minimum variance and the Gauss-Newton filtersChapter 10: The master control algorithms and goodness-of-fitPart 3: Recursive FilteringChapter 11: The Kalman and Swerling filtersChapter 12: Polynomial filtering - 1Chapter 13: Polynomial filtering - 2