Design and Implementation of Robust Particle Filter Algorithms under Student-t Measurement Distribution

Zongyuan WANG; Weidong ZHOU

doi:10.11999/JEIT190144

Volume 41 Issue 12

Dec. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(12): 2957-2964

Zongyuan WANG, Weidong ZHOU. Design and Implementation of Robust Particle Filter Algorithms under Student-t Measurement Distribution[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2957-2964. doi: 10.11999/JEIT190144

Citation:

Zongyuan WANG, Weidong ZHOU. Design and Implementation of Robust Particle Filter Algorithms under Student-t Measurement Distribution[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2957-2964. doi: 10.11999/JEIT190144

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Design and Implementation of Robust Particle Filter Algorithms under Student-t Measurement Distribution

doi: 10.11999/JEIT190144

Zongyuan WANG^{1, 2},
Weidong ZHOU^{1
,
,}

1.
College of Automation, Harbin Engineering University, Harbin 150001, China
2.
College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (61773133), The Fundamental Research Funds of the Central Universities (3072019CF2419)

Received Date: 2019-03-13
Rev Recd Date: 2019-07-23

Available Online: 2019-07-27

Publish Date: 2019-12-01

Abstract

Abstract

Outliers are non-Gaussian measurement values far from the bulk of data. In practical transmission, the signals added with outlier often have the heavy-tailed property. Particle filter is based on the Bayesian framework and applicable to the non-linear and non-Gaussian system. However, measurement noise with outlier degrades the performance of particle filter. In this paper, student-t distribution is used to model the measurement noise, combined with Variational Bayes (VB), a novel particle filter Marginalized Particle Filter with VB Mean(MPF-VBM) is designed, which can estimate all parameters of t-distributed measurement distribution including mean parameter as well as state. Further, particle filter with noise correlation (MPF-VBM-COR) at the same epoch which is applicable to time variant measurement noise is developed. For verifying the performances of the proposed algorithms, the simulations on the typical univariate non-stationary growth model are performed under the different noise conditions in detail. The outcomes show that the proposed two algorithms of MPF-VBM and MPF-VBM-COR (MPF-VBM-Corrlation) have the superior performances to the compared ones.
- Particle filter,
- Variational Bayes(VB),
- Mean estimation,
- Noise correlation,
- Student-t distribution

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References(15)

References

XU Long, MA Kemao, LI Wenshuo, et al. Particle filtering for networked nonlinear systems subject to random one-step sensor delay and missing measurements[J]. Neurocomputing, 2018, 275: 2162–2169. doi: 10.1016/j.neucom.2017.10.059

STORVIK G. Particle filters for state-space models with the presence of unknown static parameters[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 281–289. doi: 10.1109/78.978383

SAHA S, ÖZKAN E, GUSTAFSSON F, et al. Marginalized particle filters for Bayesian estimation of Gaussian noise parameters[C]. The 13th International Conference on Information Fusion, Edinburgh, UK, 2010: 1–8. doi: 10.1109/ICIF.2010.5712016.

ÖZKAN E, ŠMÍDL V, SAHA S, et al. Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters[J]. Automatica, 2013, 49(6): 1566–1575. doi: 10.1016/j.automatica.2013.02.046

ZHAO Yujia, FATEHI A, and HUANG Biao. Robust estimation of ARX models with time varying time delays using variational Bayesian approach[J]. IEEE Transactions on Cybernetics, 2018, 48(2): 532–542. doi: 10.1109/TCYB.2016.2646059

PICHÉ R, SÄRKKÄ S, and HARTIKAINEN J. Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution[C]. 2012 IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain, 2012: 1–6. doi: 10.1109/MLSP.2012.6349794.

ZHANG Yonggang, JIA Guangle, LI Ning, et al. A novel adaptive Kalman filter with colored measurement noise[J]. IEEE Access, 2018, 6: 74569–74578. doi: 10.1109/ACCESS.2018.2883040

HUANG Yulong, ZHANG Yonggang, LI Ning, et al. A novel robust Student's t-based Kalman filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3): 1545–1554. doi: 10.1109/TAES.2017.2651684

XU Dingjie, SHEN Chen, and SHEN Feng. A robust particle filtering algorithm with non-Gaussian measurement noise using student-t distribution[J]. IEEE Signal Processing Letters, 2014, 21(1): 30–34. doi: 10.1109/LSP.2013.2289975

AIT-EL-FQUIH B and HOTEIT I. A variational Bayesian multiple particle filtering scheme for large-dimensional systems[J]. IEEE Transactions on Signal Processing, 2016, 64(20): 5409–5422. doi: 10.1109/TSP.2016.2580524

GAO Wei, LI Jingchun, ZHOU Guangtao, et al. Adaptive Kalman filtering with recursive noise estimator for integrated SINS/DVL systems[J]. The Journal of Navigation, 2015, 68(1): 142–161. doi: 10.1017/S0373463314000484

ZHU Hao, LEUNG H, and HE Zhongshi. State estimation in unknown non-Gaussian measurement noise using variational Bayesian technique[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2601–2614. doi: 10.1109/TAES.2013.6621839

CHEN J and MA L. Particle filtering with correlated measurement and process noise at the same time[J]. IET Radar, Sonar & Navigation, 2011, 5(7): 726–730. doi: 10.1049/iet-rsn.2010.0365

SAHA S and GUSTAFSSON F. Particle filtering with dependent noise processes[J]. IEEE Transactions on Signal Processing, 2012, 60(9): 4497–4508. doi: 10.1109/TSP.2012.2202653

WANG Zongyuan and ZHOU Weidong. Robust linear filter with parameter estimation under student-t measurement distribution[J]. Circuits, Systems, and Signal Processing, 2019, 38(6): 2445–2470. doi: 10.1007/s00034-018-0972-8

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