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
收藏
页码：3574 / 3578
页数：5
相关论文
共 27 条
  • [11] A ROBUST GAUSSIAN APPROXIMATE FILTER FOR NONLINEAR SYSTEMS WITH HEAVY TAILED MEASUREMENT NOISES
    Huang, Yulong
    Zhang, Yonggang
    Li, Ning
    Chambers, Jonathon
    2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 4209 - 4213
  • [12] Maximum total correntropy adaptive filtering against heavy-tailed noises
    Wang, Fei
    He, Yicong
    Wang, Shiyuan
    Chen, Badong
    SIGNAL PROCESSING, 2017, 141 : 84 - 95
  • [13] Student's t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises
    Zhu, Jiangbo
    Xie, Weixin
    Liu, Zongxiang
    REMOTE SENSING, 2023, 15 (17)
  • [14] Reconstructed Variational Bayesian Kalman Filter Under Heavy-Tailed and Skewed Noises
    Wang, Ke
    Wu, Panlong
    Zhao, Baochen
    Kong, Lingqi
    He, Shan
    IEEE SIGNAL PROCESSING LETTERS, 2024, 31 : 2405 - 2409
  • [15] IMM Based Sequential Fault-tolerant Fusion Estimation with Heavy-tailed Noises
    Jia, Di
    Jiang, Lu
    Chen, Tianhua
    Xu, Jiping
    Wang, Xiaoyi
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 499 - 504
  • [16] Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise
    Vukovic, Najdan
    Miljkovic, Zoran
    NEURAL NETWORKS, 2015, 63 : 31 - 47
  • [17] Robust adaptive filtering for extended target tracking with heavy-tailed noise in clutter
    Gao, Lei
    Jing, Zhongliang
    Li, Minzhe
    Pan, Han
    IET SIGNAL PROCESSING, 2018, 12 (07) : 826 - 835
  • [18] Research on Dynamic Measurement Method of Drilling Tool Attitude Near Bit Based on Suppression of Heavy-Tailed Measurement Noise
    Yang, Hai
    Gao, Shanjun
    Liang, Haibo
    Luo, Shun
    Zhang, Pengyuan
    IEEE SENSORS JOURNAL, 2023, 23 (16) : 18384 - 18395
  • [19] Robust cooperative target tracking under heavy-tailed non-Gaussian localization noise
    Chen X.-B.
    Chen L.
    Liang S.-R.
    Hu Y.
    Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2022, 56 (05): : 967 - 976
  • [20] A new robust ELM method based on a Bayesian framework with heavy-tailed distribution and weighted likelihood function
    Ning, Kefeng
    Liu, Min
    Dong, Mingyu
    NEUROCOMPUTING, 2015, 149 : 891 - 903
123