Gaussian sum filtering methods for nonlinear non-Gaussian models

被引：3

作者：

Lin Q. ^{[1
]}

Yin J.-J. ^{[1
]}

Zhang J.-Q. ^{[1
]}

Hu B. ^{[1
]}

机构：

[1] Electronic Engineering Dept., Fudan Univ.

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2010年 / 32卷 / 12期

关键词：

Extended Kalman sum filter; Gauss-Hermite sum filter; Nonlinear non-Gaussian model; Signal processing;

D O I：

10.3969/j.issn.1001-506X.2010.12.01

中图分类号：

学科分类号：

摘要：

The Gaussian sum recursive algorithms for nonlinear non-Gaussian state space models, on the assumption that the process and measurement noises are denoted by Gaussian-sums, is firstly deduced. And then the corresponding extended Kalman sum filter (EKSF) and the Gauss-Hermite sum filter (GHSF) are proposed, which use the extended Kalman filter (EKF) and Gauss-Hermite filter (GHF) as the Gaussian sub-filter respectively. The analysis shows that the existing Gaussian sum filtering algorithms are nothing but special cases of the deduced algorithm. The simulation results show that the proposed EKSF and GHSF can deal with the state estimation of the nonlinear non-Gaussian models effectively, and only consume about 5% and 6% of the computing time required by the Gaussian sum particle filter (GSPF), while the consistent filtering performance is kept.

引用

页码：2493 / 2499

页数：6

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