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.

• 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

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.

• 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

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.

• 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