Robust Gaussian approximate filter and smoother with colored heavy tailed measurement noise

被引：0

作者：

Huang Y.-L. ^{[1
]}

Zhang Y.-G. ^{[1
]}

Wu Z.-M. ^{[2
]}

Li N. ^{[1
]}

Wang G. ^{[3
]}

机构：

[1] College of Automation, Harbin Engineering University, Harbin

[2] School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin

[3] China Ship Development and Design Center, Wuhan

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2017年 / 43卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Colored heavy tailed measurement noise; Nonlinear state estimation; State augmentation approach; Student's t distribution; Variational Bayesian;

D O I：

10.16383/j.aas.2017.c150810

中图分类号：

学科分类号：

摘要：

In this paper, new robust Gaussian approximate (GA) filter and smoother are proposed to solve the problem of nonlinear state estimation with colored heavy tailed measurement noise. Firstly, the nonlinear state estimation problem with one-step delayed state and white heavy tailed measurement noise after measurement differencing is transformed into a standard nonlinear state estimation problem with heavy tailed measurement noise based on the state augmentation approach. Secondly, new GA filter and smoother are designed for the problem of unknown scale matrix and degrees of freedom (DOF) parameter of noise of the model after measurement differencing. The state, scale matrix and DOF parameter are estimated simultaneously by building the conjugate prior distributions for unknown parameters and estimated state and using variational Bayesian approach. Finally, the efficiency and superiority of the proposed robust GA filter and smoother with colored heavy tailed measurement noise, as compared with existing method, are shown in the simulation of target tracking. Copyright © 2017 Acta Automatica Sinica. All rights reserved.

引用

页码：114 / 131

页数：17

共 33 条

[1]

Zhang Y.-G., Huang Y.-L., Zhao L., A general framework solution to Gaussian filter with multiple-step randomly-delayed measurements, Acta Automatica Sinica, 41, 1, pp. 122-135, (2015)

[2]

Zhang Y.-G., Huang Y.-L., Li N., Zhao L., Conditional posterior Cramér-Rao lower bound for nonlinear sequential Bayesian estimation with one-step randomly delayed measurements, Acta Automatica Sinica, 41, 3, pp. 559-574, (2015)

[3]

Lei M., van Wyk B.J., Qi Y., Online estimation of the approximate posterior Cramer-Rao lower bound for discrete-time nonlinear filtering, IEEE Transactions on Aerospace and Electronic Systems, 47, 1, pp. 37-57, (2011)

[4]

Arasaratnam I., Haykin S., Cubature Kalman filters, IEEE Transactions on Automatic Control, 54, 6, pp. 1254-1269, (2009)

[5]

Arasaratnam I., Haykin S., Cubature Kalman smoothers, Automatica, 47, 10, pp. 2245-2250, (2011)

[6]

Sarmavuori J., Sarkka S., Fourier-Hermite Kalman filter, IEEE Transactions on Automatic Control, 57, 6, pp. 1511-1515, (2012)

[7]

Sarmavuori J., Sarkka S., Fourier-Hermite Rauch-Tung-Striebel smoother, Proceedings of the 20th European Signal Processing Conference, pp. 2109-2113, (2012)

[8]

Jia B., Xin M., Cheng Y., High-degree cubature Kalman filter, Automatica, 49, 2, pp. 510-518, (2013)

[9]

Jia B., Xin M., A new class of nonlinear Rauch-Tung-Striebel cubature Kalman smoothers, ISA Transactions, 55, 3, pp. 72-80, (2015)

[10]

Zhang Y.-G., Huang Y.-L., Wu Z.-M., Li N., A high order unscented Kalman filtering method, Acta Automatica Sinica, 40, 5, pp. 838-848, (2014)

← 1 2 3 4 →