A New Approach to Linear Filtering and Prediction Problems

A New Approach to Linear Filtering and Prediction Problems
R. E. Kalman
30 min
Peer Reviewed Paper

A transcription of R.E. Kalman's seminal paper. Transcribed by John Lukesh, 20 January 2002

The classical filtering and prediction problem is re-examined using the Bode- Shannon representation of random processes and the “state transition” method of analysis of dynamic systems. New results are:

(1) The formulation and methods of solution of the problem apply without modifica- tion to stationary and nonstationary statistics and to growing-memory and infinite- memory filters.

(2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the co- efficients of the difference (or differential) equation of the optimal linear filter are ob- tained without further calculations.

(3) The filtering problem is shown to be the dual of the noise-free regulator problem.

The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.



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