The Kalman Filter was invented to solve a problem in spacecraft navigation, but the technique is relevant not only to navigation but also to other problems where incomplete or inconsistant observations must be combined with a (possibly incomplete) state of a system. This includes such problems as sensor fusion, robot state estimation, combining oceanography and meteorology observations with models, data assimilation and adaptive estimation/controls.

The paper du Plessis, R.M., 1967; Poor man's explanation of Kalman Filters or How I stopped worrying and learned to love matrix inversion is a must have classic. It is the starting point for all of the above problems. It belongs on the bookshelf of every engineer.

Taygeta Scientific Incorporated, has aquired the rights to republish the report. And now has reprinted it (the reprint consists of the original report, and a bibliography. It has the ISBN number, 0-9661016-0-1). For $24.95 you can now get your very own copy!