Optimal recursive filtering using Gaussian mixture model

被引：0

作者：

Bilik, Igal ^{[1
]}

Tabrikian, Joseph ^{[1
]}

机构：

[1] Ben Gurion Univ Negev, Dept Elect & Comp Engn, IL-84105 Beer Sheva, Israel

来源：

2005 IEEE/SP 13TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP), VOLS 1 AND 2 | 2005年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Kalman filter is an optimal recursive estimator of the system state in terms of minimum-mean-square error (MMSE) under linear Gaussian assumptions. The Gaussianity assumption is not; satisfied in many applications, such as dynamic channel estimation in mobile communications, maneuvering target tracking and speech enhancement,. In this paper, the MMSE estimator for linear, non-Gaussian problems is presented, where the Gaussian mixture model is used for non-Gaussian distributions. The resulting recursive algorithm, named as non-Gaussian Kalman filter (NGKF), is composed of several conventional Kalman filters combined in an optimal manner. The performance of the proposed NGKF, is compared to the Kalman and particle filters via simulations. It is shown that the proposed NGKF outperforms both the Kalman and particle filters.

引用

页码：362 / 367

页数：6

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