Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)

A Survey of PHD Filtering Method Based on Random Finite Set

Authors
Yan Song, Jianwang Hu, Bing Ji
Corresponding Author
Yan Song
Available Online May 2017.
DOI
10.2991/icmeit-17.2017.36How to use a DOI?
Keywords
random finite set, multi-target tracking, probability hypothesis density filter
Abstract

Probability hypothesis density filter based on Random Finite Set(RFS)recently become a research hotspot for multi-sensor information fusion in the world. An overview of the emergence, the development and the present research situation of the PHD filter in target tracking is presented here. Special attention is paid to the following areas:multi-target tracking method, method of PHD filter based on random finite set and the research status of probability hypothesis density filter. Finally, the future research directions in probability hypothesis density.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
Series
Advances in Computer Science Research
Publication Date
May 2017
ISBN
10.2991/icmeit-17.2017.36
ISSN
2352-538X
DOI
10.2991/icmeit-17.2017.36How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yan Song
AU  - Jianwang Hu
AU  - Bing Ji
PY  - 2017/05
DA  - 2017/05
TI  - A Survey of PHD Filtering Method Based on Random Finite Set
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 199
EP  - 204
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.36
DO  - 10.2991/icmeit-17.2017.36
ID  - Song2017/05
ER  -