卡尔曼滤波器
贝叶斯概率
数学优化
随机控制
计算机科学
贝叶斯估计量
非线性系统
线性二次高斯控制
高斯分布
计算
估计
数学
最优控制
算法
人工智能
工程类
物理
量子力学
系统工程
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:1964-10-01
卷期号:9 (4): 333-339
被引量:563
标识
DOI:10.1109/tac.1964.1105763
摘要
In this paper, a general class of stochastic estimation and control problems is formulated from the Bayesian Decision-Theoretic viewpoint. A discussion as to how these problems can be solved step by step in principle and practice from this approach is presented. As a specific example, the closed form Wiener-Kalman solution for linear estimation in Gaussian noise is derived. The purpose of the paper is to show that the Bayesian approach provides; 1) a general unifying framework within which to pursue further researches in stochastic estimation and control problems, and 2) the necessary computations and difficulties that must be overcome for these problems. An example of a nonlinear, non-Gaussian estimation problem is also solved.
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