对线性滤波和预测问题的一种新的解决方法（即最原理卡尔曼滤波算法）。
The classical filtering and prediction problem is re-examined using the Bode-
Shannon representation of random processes and the “state transition” method ofanalysis of dynamic systems. New results are:
(1) The formulation and methods of solution of the problem apply without modificationto stationary and nonstationary statistics and to growing-memory and infinitememoryfilters.
(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 coefficients of the difference (or differential) equation of the optimal linear filter are obtained 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.