NONLINEAR FILTERING - A WEIGHTED MEAN SQUARES APPROACH AND A BAYESIAN ONE VIA THE MAXIMUM-ENTROPY PRINCIPLE

被引：8

作者：

JUMARIE, G

机构：

[1] Department of Mathematics and Computer Science, Université du Québec à Montréal, H3C 3P8, St A, Montréal, QUE

来源：

SIGNAL PROCESSING | 1990年 / 21卷 / 04期

关键词：

Bayesian estimation; Fokker-Planck equation; maximum path entropy; mean-squares estimation; Nonlinear filtering; stochastic systems;

D O I：

10.1016/0165-1684(90)90102-5

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper suggests two approaches to nonlinear filtering, which make abdication of the differential equation of the conditional probability density given the observation. In the first one, which refers to the statistical point of view, one derives the trajectory of the state estimation by minimizing a weighted cost function which can be thought of as an extended mean-squares criterion, and the result is compared with the Kalman filter (in the linear case!). The second approach is the Bayesian one, and the basic problem is to determine the probability density of the state as it is defined by the Fokker-Planck equation. To this end one suggests two approaches via the maximum entropy principle. The first one refers to the state moments of the system, while the second one involves a slight extension of this principle via the time integral of the entropy. © 1990.

引用

页码：323 / 338

页数：16

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