Nonlinear filtering of non-Gaussian noise

被引：45
|
作者：
Plataniotis, KN
Androutsos, D
Venetsanopoulos, AN
机构：
[1] Dept. of Elec. and Comp. Engineering, University of Toronto, Toronto
来源：
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS | 1997年 / 19卷 / 02期
关键词：
adaptive algorithms; Gaussian mixtures; Kalman filter; narrowband interference; non-Gaussian filtering;
D O I：
10.1023/A:1007974400149
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.
引用
收藏
页码：207 / 231
页数：25
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