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An Adaptive Robust Student’s t-Based Kalman Filter Based on Multi-sensor Fusion

  • Dapeng Wang
  • , Hai Zhang*
  • , Hongliang Huang
  • *此作品的通讯作者
  • Beihang University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In practical applications, Kalman filter and its variants such as UKF may suffer from the time-varying measurement noise and process-error. Especially, when the process-error is heavy-tailed probability distribution, the Gaussian assumption would be no longer as accurately as expected. Aiming at the time-varying measurement noise and the situation with heavy-tailed process-error problems, in this paper, a new algorithm is proposed based on Student’s t-distribution and multi-sensor information fusion. The robustness of the proposed algorithm is guaranteed by the timely estimation of the measurement noise, and the adaptiveness is realized by replacing the Gaussian by the Student’s t-distribution. The Kullback-Leible Divergence (KLD) is used as the criterion for distinguishing the Gaussian distribution from the Student’s t-distribution. Finally, a challenging target tracking example is presented and the simulation results show that the proposed algorithm achieves a higher accuracy than the other algorithms.

源语言英语
主期刊名Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings
编辑Honghai Liu, Weihong Ren, Zhouping Yin, Lianqing Liu, Li Jiang, Guoying Gu, Xinyu Wu
出版商Springer Science and Business Media Deutschland GmbH
603-613
页数11
ISBN(印刷版)9783031138218
DOI
出版状态已出版 - 2022
活动15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 - Harbin, 中国
期限: 1 8月 20223 8月 2022

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13456 LNAI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议15th International Conference on Intelligent Robotics and Applications, ICIRA 2022
国家/地区中国
Harbin
时期1/08/223/08/22

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