Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

被引:9
|
作者
Hou, Liming [1 ]
Lian, Feng [1 ]
Tan, Shuncheng [2 ,3 ]
Xu, Congan [3 ]
de Abreu, Giuseppe Thadeu Freitas [4 ]
机构
[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Minist Educ, Key Lab Intelligent Networks & Network Secur, Xian 710049, Peoples R China
[2] Nanjing Res Inst Elect Technol, Nanjing 210039, Peoples R China
[3] Naval Aviat Univ, Inst Informat Fus, Yantai 264001, Peoples R China
[4] Jacobs Univ Bremen, Dept Comp Sci & Elect Engn, D-28759 Bremen, Germany
基金
中国国家自然科学基金;
关键词
Noise measurement; Target tracking; Probability density function; Covariance matrices; Gaussian distribution; Bayes methods; Gamma distribution; Generalized labeled multi-Bernoulli filter; multitarget tracking (MTT); variational Bayesian (VB); non-stationary; heavy-tailed measurement noise (HTMN); unknown and time-varying mean; RANDOM FINITE SETS; TARGET TRACKING; MODEL;
D O I
10.1109/ACCESS.2021.3092021
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student's t-distribution with unknown and time-varying mean. The proposed filter relaxes the restrictive assumption that the mean of HTMN is zero, and can effectively deal with MTT under the condition that the mean of HTMN is unknown and time-varying. The variational Bayesian (VB) approximation is applied in the GLMB filtering framework with the augmented state. The marginal likelihood function is obtained via minimizing the Kullback-Leibler divergence by the variational lower bound. The simulation results demonstrate that the proposed filter can effectively track multiple targets in both linear and nonlinear scenarios when the mean of HTMN is unknown and time-varying.
引用
收藏
页码:94438 / 94453
页数:16
相关论文
共 50 条
  • [21] Multiple Extended Target Tracking in the Presence of Heavy-Tailed Noise Using Multi-Bernoulli Filtering Method
    Chen H.
    Zhang X.-X.
    Zidonghua Xuebao/Acta Automatica Sinica, 2023, 49 (07): : 1573 - 1586
  • [22] A Merge/Split Algorithm for Multitarget Tracking Using Generalized Labeled Multi-Bernoulli Filters
    Chen, Lingji
    SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXXI, 2022, 12122
  • [23] Measurement Driven Birth Model for the Generalized Labeled Multi-Bernoulli Filter
    Lin, Shoufeng
    Vo, Ba Tuong
    Nordholm, Sven E.
    2016 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (ICCAIS), 2016, : 94 - 99
  • [24] A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise
    Yu, Benru
    Gu, Hong
    Su, Weimin
    SENSORS, 2024, 24 (09)
  • [25] 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)
  • [26] Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics
    Qiu Hao
    Huang Gaoming
    Gao Jun
    Chinese Journal of Aeronautics, 2016, 29 (05) : 1378 - 1384
  • [27] Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics
    Qiu Hao
    Huang Gaoming
    Gao Jun
    CHINESE JOURNAL OF AERONAUTICS, 2016, 29 (05) : 1378 - 1384
  • [28] Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics
    Qiu Hao
    Huang Gaoming
    Gao Jun
    Chinese Journal of Aeronautics , 2016, (05) : 1378 - 1384
  • [29] Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian
    Li, Peng
    Wang, Wenhui
    Qiu, Junda
    You, Congzhe
    Shu, Zhenqiu
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2022, 16 (03): : 908 - 928
  • [30] Robust Pitch Estimation and Tracking For Speakers Based on Subband Encoding and The Generalized Labeled Multi-Bernoulli Filter
    Lin, Shoufeng
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2019, 27 (04) : 827 - 841