信息融合理论研究进展:基于变分贝叶斯的联合优化

Quan Pan, Yu Mei Hu, Hua Lan, Shuai Sun, Zeng Fu Wang, Feng Yang

科研成果: 期刊稿件文献综述同行评审

23 引用 (Scopus)

摘要

By reviewing the development of information fusion theory in recent years, this paper analyzes the problems of target tracking systems, such as nonlinearity, multi-mode, deep coupling, networking, high-dimensionality and unknown disturbance input, and points out the necessity of joint optimization in target tracking system. Furthermore, several joint optimization methods, including the joint detection and estimation, joint clustering and estimation, joint association and estimation, joint decision and estimation are discussed. Meanwhile, we emphatically introduce the integrated optimization method based on the variational Bayesian theory that provides a unified framework of joint identification and estimation. Taking over-the-horizon radar as an application background, we give a general joint optimization method for the multi-path multi-mode multi-target tracking system in this paper. In addition, future research directions of the variational Bayesian theory in the field of target tracking are discussed.

投稿的翻译标题Information Fusion Progress: Joint Optimization Based on Variational Bayesian Theory
源语言繁体中文
页(从-至)1207-1223
页数17
期刊Zidonghua Xuebao/Acta Automatica Sinica
45
7
DOI
出版状态已出版 - 7月 2019

关键词

  • Information fusion
  • Joint optimization
  • State estimation
  • Target tracking
  • Variational Bayesian theory

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