Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise

Ruxuan He; Xiaoxue Feng; Shuihui Li; Feng Pan; Ning Pu

doi:10.1109/CCDC52312.2021.9602396

Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise

Ruxuan He, Xiaoxue Feng, Shuihui Li, Feng Pan, Ning Pu

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In the actual application scenario of target tracking and positioning, the target is affected by maneuvering interference, measurement outliers, and abnormal values, and system noise and measurement noise may obey non-Gaussian heavy-tailed and skew distribution. In this case, the traditional Kalman filter based on Gaussian noise modeling fails to obtain the satisfying estimation performance. Aiming at non-Gaussian thick-tailed noise, this paper proposes a hierarchical multivariate Gaussian-Skew T mixture model. Using the variational Bayesian theory, the estimation of the state probability density function is converted into two probability density functions of the unknown noise and the nonlinear state. Using Bayesian inference, an iterative algorithm for joint estimation of state and unknown noise is proposed. And the effectiveness of the algorithm is verified in the target tracking simulation experiment and UWB positioning experiment.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6191-6198
Number of pages	8
ISBN (Electronic)	9781665440899
DOIs	https://doi.org/10.1109/CCDC52312.2021.9602396
Publication status	Published - 2021
Event	33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China Duration: 22 May 2021 → 24 May 2021

Publication series

Name	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference	33rd Chinese Control and Decision Conference, CCDC 2021
Country/Territory	China
City	Kunming
Period	22/05/21 → 24/05/21

Keywords

Heavy-tailed noise
Multivariate Gaussian-Skew T mixture noise
Nonlinear system
Variational bayes

Access to Document

10.1109/CCDC52312.2021.9602396

Cite this

He, R., Feng, X., Li, S., Pan, F., & Pu, N. (2021). Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021 (pp. 6191-6198). (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC52312.2021.9602396

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title = "Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise",

abstract = "In the actual application scenario of target tracking and positioning, the target is affected by maneuvering interference, measurement outliers, and abnormal values, and system noise and measurement noise may obey non-Gaussian heavy-tailed and skew distribution. In this case, the traditional Kalman filter based on Gaussian noise modeling fails to obtain the satisfying estimation performance. Aiming at non-Gaussian thick-tailed noise, this paper proposes a hierarchical multivariate Gaussian-Skew T mixture model. Using the variational Bayesian theory, the estimation of the state probability density function is converted into two probability density functions of the unknown noise and the nonlinear state. Using Bayesian inference, an iterative algorithm for joint estimation of state and unknown noise is proposed. And the effectiveness of the algorithm is verified in the target tracking simulation experiment and UWB positioning experiment.",

keywords = "Heavy-tailed noise, Multivariate Gaussian-Skew T mixture noise, Nonlinear system, Variational bayes",

author = "Ruxuan He and Xiaoxue Feng and Shuihui Li and Feng Pan and Ning Pu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",

year = "2021",

doi = "10.1109/CCDC52312.2021.9602396",

language = "English",

series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

address = "United States",

}

He, R, Feng, X, Li, S, Pan, F & Pu, N 2021, Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise. in Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 6191-6198, 33rd Chinese Control and Decision Conference, CCDC 2021, Kunming, China, 22/05/21. https://doi.org/10.1109/CCDC52312.2021.9602396

Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise. / He, Ruxuan; Feng, Xiaoxue; Li, Shuihui et al.
Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 6191-6198 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise

AU - He, Ruxuan

AU - Feng, Xiaoxue

AU - Li, Shuihui

AU - Pan, Feng

AU - Pu, Ning

PY - 2021

Y1 - 2021

N2 - In the actual application scenario of target tracking and positioning, the target is affected by maneuvering interference, measurement outliers, and abnormal values, and system noise and measurement noise may obey non-Gaussian heavy-tailed and skew distribution. In this case, the traditional Kalman filter based on Gaussian noise modeling fails to obtain the satisfying estimation performance. Aiming at non-Gaussian thick-tailed noise, this paper proposes a hierarchical multivariate Gaussian-Skew T mixture model. Using the variational Bayesian theory, the estimation of the state probability density function is converted into two probability density functions of the unknown noise and the nonlinear state. Using Bayesian inference, an iterative algorithm for joint estimation of state and unknown noise is proposed. And the effectiveness of the algorithm is verified in the target tracking simulation experiment and UWB positioning experiment.

AB - In the actual application scenario of target tracking and positioning, the target is affected by maneuvering interference, measurement outliers, and abnormal values, and system noise and measurement noise may obey non-Gaussian heavy-tailed and skew distribution. In this case, the traditional Kalman filter based on Gaussian noise modeling fails to obtain the satisfying estimation performance. Aiming at non-Gaussian thick-tailed noise, this paper proposes a hierarchical multivariate Gaussian-Skew T mixture model. Using the variational Bayesian theory, the estimation of the state probability density function is converted into two probability density functions of the unknown noise and the nonlinear state. Using Bayesian inference, an iterative algorithm for joint estimation of state and unknown noise is proposed. And the effectiveness of the algorithm is verified in the target tracking simulation experiment and UWB positioning experiment.

KW - Heavy-tailed noise

KW - Multivariate Gaussian-Skew T mixture noise

KW - Nonlinear system

KW - Variational bayes

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U2 - 10.1109/CCDC52312.2021.9602396

DO - 10.1109/CCDC52312.2021.9602396

M3 - Conference contribution

AN - SCOPUS:85125200801

T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

SP - 6191

EP - 6198

BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd Chinese Control and Decision Conference, CCDC 2021

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He R, Feng X, Li S, Pan F, Pu N. Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 6191-6198. (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). doi: 10.1109/CCDC52312.2021.9602396

Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise

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