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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.
Contact Us for Consulting and/or Training for Kalman filters.
A reading list for Kalman filtering.Reading list
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 scientist and engineer dealing with real world data.
GOOD NEWS!!! The du Plessis paper has been an underground classic for years and has often been hard to find. Taygeta Scientific Incorporated, has aquired the rights to republish the report. This reprint consists of the original report, and a bibliography. It has the ISBN number, 0-9661016-0-1. Order your copy now
Kalman Filter Package in C++.
(Updated 27 Jan 02 with simplified model API)
A Kalman filter simulator for the HP48G
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