MAXIMUM-LIKELIHOOD-ESTIMATION OF A CLASS OF CHAOTIC SIGNALS

被引：61

作者：

PAPADOPOULOS, HC ^{[1
]}

WORNELL, GW ^{[1
]}

机构：

[1] MIT,ELECTR RES LAB,CAMBRIDGE,MA 02139

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 1995年 / 41卷 / 01期

关键词：

CHAOS; NONLINEAR DYNAMICS; RECURSIVE ESTIMATION; MAXIMUM LIKELIHOOD; KALMAN FILTERING;

D O I：

10.1109/18.370091

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The chaotic sequences corresponding to tent map dynamics are potentially attractive in a range of engineering applications'. Optimal estimation algorithms for signal filtering, prediction, and smoothing in the presence of white Gaussian noise are derived for this class of sequences based on the method of Maximum Likelihood. The resulting algorithms are highly nonlinear but have convenient recursive implementations that are efficient both in terms of computation and storage. Performance evaluations are also included and compared with the associated Cramer-Rao bounds.

引用

页码：312 / 317

页数：6