OPTIMAL ESTIMATION IN PRESENCE OF UNKNOWN PARAMETERS

被引：58

作者：

HILBORN, CG

LAINIOTI.DG

机构：

[1] Bell Telephone Laboratories, Inc., Winston-Salem, N. C.

[2] Department of Electrical Engineering, University of Texas, Austin, Tex.

来源：

IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICS | 1969年 / SSC5卷 / 01期

关键词：

D O I：

10.1109/TSSC.1969.300242

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

An adaptive approach is presented for optimal estimation of a sampled stochastic process with finite-state unknown parameters. It is shown that, for processes with an implicit generalized Markov property, the optimal (conditional mean) state estimates can be formed from 1) a set of optimal estimates based on known parameters, and 2) a set of “learning” statistics which are recursively updated. The formulation thus provides a separation technique which simplifies the optimal solution of this class of nonlinear estimation problems. Examples of the separation technique are given for prediction of a non-Gaussian Markov process with unknown parameters and for filtering the state of a Gauss-Markov process with unknown parameters. General results are given on the convergence of optimal estimation systems operating in the presence of unknown parameters. Conditions are given under which a Bayes optimal (conditional mean) adaptive estimation system will converge in performance to an optimal system which is “told” the value of unknown parameters. Copyright © 1969 by The Institute of Electrical and Electronics Engineers, Inc.

引用

页码：38 / &

共 13 条

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