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
收藏
页码:362 / 367
页数:6
相关论文
共 50 条
[21]   Using the Gini Index for a Gaussian Mixture Model [J].
Laura Lopez-Lobato, Adriana ;
Lorena Avendano-Garrido, Martha .
ADVANCES IN COMPUTATIONAL INTELLIGENCE, MICAI 2020, PT II, 2020, 12469 :403-418
[22]   Speaker Verification Using Gaussian Mixture Model [J].
Jagtap, Shilpa S. ;
Bhalke, D. G. .
2015 INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING (ICPC), 2015,
[23]   A Novel Approach for Efficient Gaussian Mixture Model using Dynamics-motivated Optimal Excitation [J].
Kim, Taehoon ;
Jeong, Juwon ;
Kong, Taejune ;
Lee, Hyunwook ;
Oh, Sehoon .
2024 33RD INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, ISIE 2024, 2024,
[24]   Semi-supervised optimal recursive filtering and smoothing in non-Gaussian Markov switching models [J].
Zheng, Fei ;
Derrode, Stephane ;
Pieczynski, Wojciech .
SIGNAL PROCESSING, 2020, 171
[25]   Recursive Gaussian Mixture Models for Adaptive Process Monitoring [J].
Zheng, Junhua ;
Wen, Qiaojun ;
Song, Zhihuan .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2019, 58 (16) :6551-6561
[26]   A Partially Uniform Target Birth Model for Gaussian Mixture PHD/CPHD Filtering [J].
Beard, Michael ;
Vo, Ba Tuong ;
Vo, Ba-Ngu ;
Arulampalam, Sanjeev .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2013, 49 (04) :2835-2844
[27]   OPTIMAL FILTERING OF GAUSSIAN MARKOV SIGNALS IN GAUSSIAN NOISE [J].
KULMAN, NK ;
TARANKOVA, ND .
RADIOTEKHNIKA I ELEKTRONIKA, 1977, 22 (07) :1384-1389
[28]   Nonlinear optimal semi-recursive filtering [J].
Daum, F .
SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1996, 1996, 2759 :256-257
[29]   Asymptotically optimal recursive filtering of time series [J].
Shilman, S .
ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 1996, 76 :557-558
[30]   Asymptotically optimal recursive filtering of time series [J].
Z Angew Math Mech ZAMM, Suppl 3 (557)