Information Fusion Progress: Joint Optimization Based on Variational Bayesian Theory

被引：0
|
作者：
Pan Q. [1 ,2 ]
Hu Y.-M. [1 ,2 ,3 ]
Lan H. [1 ,2 ]
Sun S. [2 ,4 ]
Wang Z.-F. [1 ,2 ]
Yang F. [1 ,2 ]
机构：
[1] School of Automation, Northwestern Polytechnical University, Xi'an
[2] Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an
[3] The University of Melbourne, Melbourne, 3010, VIC
[4] RMIT University, Melbourne, 3000, VIC
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Information fusion; Joint optimization; State estimation; Target tracking; Variational Bayesian theory;
D O I：
10.16383/j.aas.c180029
中图分类号：
学科分类号：
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. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1207 / 1223
页数：16
相关论文
共 122 条
  • [11] Bosman H.H.W.J., Iacca G., Tejada A., Wotche H.J., Liotta A., Spatial anomaly detection in sensor networks using neighborhood information, Information Fusion, 33, pp. 41-56, (2017)
  • [12] Anderson C., Breimyer P., Foster S., Geyer K., Griffith J.D., Heier A., Et al., A network science approach to open source data fusion and analytics for disaster response, Proceedings of the 201518th International Conference on Information Fusion, pp. 207-214, (2015)
  • [13] Pan Q., Yu X., Cheng Y.-M., Zhang H.-C., Essential methods and progress of information fusion theory, Acta Automatica Sinica, 29, 4, pp. 599-615, (2003)
  • [14] Pan Q., Wang Z.-F., Liang Y., Yang F., Liu Z.-G., Basic methods and progress of information fusion (II), Control Theory and Applications, 29, 10, pp. 1233-1244, (2012)
  • [15] Li X.R., Jilkov V.P., Survey of maneuvering target tracking. Part V: Multiple-Model Methods, IEEE Transactions on Aerospace and Electronic Systems, 41, 4, pp. 1255-1321, (2005)
  • [16] Gustafsson F., Hendeby G., Some relations between extended and unscented Kalman filters, IEEE Transactions on Signal Processing, 60, 2, pp. 545-555, (2012)
  • [17] Julier S., Uhlmann J., Durrant-Whyte H.F., A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Transactions on Automatic Control, 45, 3, pp. 477-482, (2000)
  • [18] Arulampalam M.S., Maskell S., Gordon N., Clapp T., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, 50, 2, pp. 174-188, (2002)
  • [19] Frank A., Smyth P., Ihler A., Beyond MAP estimation with the track-oriented multiple hypothesis tracker, IEEE Transactions on Signal Processing, 62, 9, pp. 2413-2423, (2014)
  • [20] Li Q., Sun J.P., Sun W., An efficient multiple hypothesis tracker using max product belief propagation, Proceedings of the 20th International Conference on Information Fusion, pp. 1042-1049, (2017)
12345678910