A Robust Interacting Multiple Model Smoother with Heavy-Tailed Measurement Noises

被引：0

作者：

Cui, Shuai ^{[1
]}

Li, Zhi ^{[1
]}

Yang, Yanbo ^{[1
]}

机构：

[1] Xidian Univ, Sch Mechanoelect Engn, Xian, Peoples R China

来源：

2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年

关键词：

IMM smoother; Student's t-distribution; heavy-tailed measurement noises; forward and backward filtering; ALGORITHM;

D O I：

10.1109/CAC51589.2020.9327048

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Interacting multiple model (IMM) estimator has found an increasingly wide utilization in the filed of target tracking. Smoothing, uses all obtained measurements to estimate the previous state in order to provide a better estimation. However, IMM smoothers do not perform well and suffer severe performance degradation with some outliers existing in the measurement noises. This paper proposes a novel robust IMM smoother to deal with heavy-tailed measurement noises obeying the Student's t-distribution. The proposed smoother is derived from a forward filter combing with a backward filter. An example of maneuvering target tracking with heavy-tailed

引用

页码：3574 / 3578

页数：5

共 31 条

[11] A ROBUST GAUSSIAN APPROXIMATE FILTER FOR NONLINEAR SYSTEMS WITH HEAVY TAILED MEASUREMENT NOISES [J].

Huang, Yulong ;

Zhang, Yonggang ;

Li, Ning ;

Chambers, Jonathon .

2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, :4209-4213

[12] A robust filter and smoother-based expectation-maximization algorithm for bilinear systems with heavy-tailed noise [J].

Wang, Wenjie ;

Liu, Siyu ;

Jiang, Yonghua ;

Sun, Jianfeng ;

Xu, Wanxiu ;

Chen, Xiaohao ;

Dong, Zhilin ;

Jiao, Weidong .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2025, 236

[13] Maximum total correntropy adaptive filtering against heavy-tailed noises [J].

Wang, Fei ;

He, Yicong ;

Wang, Shiyuan ;

Chen, Badong .

SIGNAL PROCESSING, 2017, 141 :84-95

[14] Student's t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises [J].

Zhu, Jiangbo ;

Xie, Weixin ;

Liu, Zongxiang .

REMOTE SENSING, 2023, 15 (17)

[15] Reconstructed Variational Bayesian Kalman Filter Under Heavy-Tailed and Skewed Noises [J].

Wang, Ke ;

Wu, Panlong ;

Zhao, Baochen ;

Kong, Lingqi ;

He, Shan .

IEEE SIGNAL PROCESSING LETTERS, 2024, 31 :2405-2409

[16] IMM Based Sequential Fault-tolerant Fusion Estimation with Heavy-tailed Noises [J].

Jia, Di ;

Jiang, Lu ;

Chen, Tianhua ;

Xu, Jiping ;

Wang, Xiaoyi .

2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, :499-504

[17] Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise [J].

Vukovic, Najdan ;

Miljkovic, Zoran .

NEURAL NETWORKS, 2015, 63 :31-47

[18] Robust adaptive filtering for extended target tracking with heavy-tailed noise in clutter [J].

Gao, Lei ;

Jing, Zhongliang ;

Li, Minzhe ;

Pan, Han .

IET SIGNAL PROCESSING, 2018, 12 (07) :826-835

[19] Robust cubature Kalman filter based on generalized hyperbolic distribution for SLAM under colored heavy-tailed measurement noise [J].

Zhao, Jiaxiang ;

Wang, Guoqing .

DIGITAL SIGNAL PROCESSING, 2025, 166

[20] Research on Dynamic Measurement Method of Drilling Tool Attitude Near Bit Based on Suppression of Heavy-Tailed Measurement Noise [J].

Yang, Hai ;

Gao, Shanjun ;

Liang, Haibo ;

Luo, Shun ;

Zhang, Pengyuan .

IEEE SENSORS JOURNAL, 2023, 23 (16) :18384-18395

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