A novel multiple-outlier-robust Kalman filter

Yulong HUANG; Mingming BAI; Yonggang ZHANG

doi:10.1631/FITEE.2000642

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A novel multiple-outlier-robust Kalman filter

Regular Papers | Updated：2022-03-28

- A novel multiple-outlier-robust Kalman filter
  Enhanced Publication
- 一种新型多样野值鲁棒卡尔曼滤波器
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 3, Pages: 422-437(2022)
- Affiliations：
  
  College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin150001, China
- Author bio：
  
  [ "Yulong HUANG, first author of this invited paper, received his BS degree in automation and PhD degree in control science and engineering from the College of Automation, Harbin Engineering University (HEU), Harbin, China, in 2012 and 2018, respectively. From Nov. 2016 to Nov. 2017, he was a visiting researcher at the Electrical Engineering Department of Columbia University, New York, USA. Currently, he is an associate professor of navigation, guidance, and control in HEU. From Dec. 2019 to Dec. 2021, he was associated with the Department of Mechanical Engineering, City University of Hong Kong, as a Hong Kong Scholar. His current research interests include state estimation, intelligent information fusion, and their applications in navigation technology, such as inertial navigation, integrated navigation, intelligent navigation, and cooperative navigation." ]
  [ "Mingming BAI received the BS degree in measurement and control technology and instrumentation from the College of Automation, China University of Geosciences, Wuhan, China, in 2016. He is currently pursuing the PhD degree in control science and engineering with the HEU. His current research interests include target tracking, data fusion, multiagent systems, and state estimation theory." ]
  [ "Yonggang ZHANG, corresponding author of this invited paper, received his BS and MS degrees from the College of Automation, HEU, Harbin, China, in 2002 and 2004, respectively. He received his PhD degree in electronic engineering from Cardiff University, UK in 2007 and worked as a post-doctoral fellow at Loughborough University, UK from 2007 to 2008 in the area of adaptive signal processing. Currently, he is a professor of navigation, guidance, and control in HEU. His current research interests include signal processing, information fusion, and their applications in navigation technology, such as fiber optical gyroscope, inertial navigation, and integrated navigation. Prof. ZHANG currently serves as a corresponding expert for Front Inform Technol Electron Eng." ]
- Funds：
- DOI：10.1631/FITEE.2000642
  CLC： TP273
- Published：0 March 2022，
  
  Published Online：24 January 2022，
  
  Received：17 November 2020，
  
  Accepted：2021-03-01
- Accepted：
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YULONG HUANG, MINGMING BAI, YONGGANG ZHANG. A novel multiple-outlier-robust Kalman filter. [J]. Frontiers of information technology & electronic engineering, 2022, 23(3): 422-437.
DOI：

YULONG HUANG, MINGMING BAI, YONGGANG ZHANG. A novel multiple-outlier-robust Kalman filter. [J]. Frontiers of information technology & electronic engineering, 2022, 23(3): 422-437. DOI： 10.1631/FITEE.2000642.

摘要

针对线性离散随机系统，提出一种新型多样野值鲁棒卡尔曼滤波器（MORKF）。首先提出一种新的多重统计相似度来衡量两个随机向量各维度之间的相似性。然后，通过最大化基于多重统计相似度量的代价函数，得到所提出的MORKF。MORKF保证了迭代在弱约束下的收敛性，且本文从理论上分析了近似误差的有界性。给出了相似函数的选择策略，并与现有鲁棒方法进行比较。仿真结果验证了该滤波器的优越性。

Abstract

This paper presents a novel multiple-outlier-robust Kalman filter (MORKF) for linear stochastic discrete-time systems. A new multiple statistical similarity measure is first proposed to evaluate the similarity between two random vectors from dimension to dimension. Then

the proposed MORKF is derived via maximizing a multiple statistical similarity measure based cost function. The MORKF guarantees the convergence of iterations in mild conditions

and the boundedness of the approximation errors is analyzed theoretically. The selection strategy for the similarity function and comparisons with existing robust methods are presented. Simulation results show the advantages of the proposed filter.

关键词

卡尔曼滤波多重统计相似度量多样野值定点迭代状态估计

Keywords

Kalman filteringMultiple statistical similarity measureMultiple outliersFixed-point iterationState estimate

references

Chen BD,Liu X,Zhao HQ,et al.,2017.Maximum correntropy Kalman filter.Automatica,76:70-77.doi:10.1016/j.automatica.2016.10.004http://doi.org/10.1016/j.automatica.2016.10.004

Huang YL,Zhang YG,Li N,et al.,2016.Robust Student’st based nonlinear filter and smoother.IEEE Trans Aerosp Electron Syst,52(5):2586-2596.doi:10.1109/TAES.2016.150722http://doi.org/10.1109/TAES.2016.150722

Huang YL,Zhang YG,Li N,et al.,2017.A novel robust Student’st-based Kalman filter.IEEE Trans Aerosp Electron Syst,53(3):1545-1554.doi:10.1109/TAES.2017.2651684http://doi.org/10.1109/TAES.2017.2651684

Huang YL,Zhang YG,Zhao YX,et al.,2019a.A novel robust Gaussian-Student’st mixture distribution based Kalman filter.IEEE Trans Signal Process,67(13):3606-3620.doi:10.1109/TSP.2019.2916755http://doi.org/10.1109/TSP.2019.2916755

Huang YL,Zhang YG,Chambers JA,2019b.A novel Kullback–Leibler divergence minimization-based adaptive Student’st-filter.IEEE Trans Signal Process,67(20):5417-5432.doi:10.1109/TSP.2019.2939079http://doi.org/10.1109/TSP.2019.2939079

Huang YL,Zhang YG,Shi P,et al.,2019c.Robust Kalman filters based on Gaussian scale mixture distributions with application to target tracking.IEEE Trans Syst Man Cybern Syst,49(10):2082-2096.doi:10.1109/TSMC.2017.2778269http://doi.org/10.1109/TSMC.2017.2778269

Huang YL,Zhang YG,Zhao YX,et al.,2020.A novel outlier-robust Kalman filtering framework based on statistical similarity measure.IEEE Trans Autom Contr,66(6):2677-2692.doi:10.1109/TAC.2020.3011443http://doi.org/10.1109/TAC.2020.3011443

Huber PJ,2011.Robust statistics. In:Lovric M (Ed.),International Encyclopedia of Statistical Science.Springer,Berlin, Germany.

Piché R,Särkkä S,Hartikainen J,2012.Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate Student-t distribution.Proc IEEE Int Workshop on Machine Learning for Signal Processing, p.1-6.doi:10.1109/MLSP.2012.6349794http://doi.org/10.1109/MLSP.2012.6349794

Roth M,Ardeshiri T,Özkan E,et al.,2017.Robust Bayesian filtering and smoothing using Student’st distribution.https://arxiv.org/abs/1703.02428https://arxiv.org/abs/1703.02428

Simon D,2006.Optimal State Estimation: Kalman,H∞, and Nonlinear Approaches.John Wiley,Hoboken, USA.

Ting JA,Theodorou E,Schaal S,2007.Learning an outlier-robust Kalman filter.Proc 18th European Conf on Machine Learning, p.748-756.doi:10.1007/978-3-540-74958-5_76http://doi.org/10.1007/978-3-540-74958-5_76

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