Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

A Study on Dispersion Law of Underwater Threat based on Monte Carlo Algorithm

Authors
Shufen LIU, Ji HOU, Lu HAN
Corresponding Author
Shufen LIU
Available Online July 2017.
DOI
10.2991/eia-17.2017.41How to use a DOI?
Keywords
monte carlo; analysis of underwater target distribution; probability density of underwater target position distribution
Abstract

At present, it is impossible to detect the underwater target accurately found at a distance by equipment such as multi-sonars due to the limitation of underwater target detection technology. A method using Monte Carlo algorithm to confirm the dispersion law of underwater threat according to the target motion elements is presented. Combined with the probability theory, probability density of underwater threat locating is calculated. Finally, the distribution law of the typical situation is simulated, and the simulation results show that the profile of probability density distribution of the threat is consistent with the result of the threat distribution law.

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 2017 International Conference on Electronic Industry and Automation (EIA 2017)
Series
Advances in Intelligent Systems Research
Publication Date
July 2017
ISBN
10.2991/eia-17.2017.41
ISSN
1951-6851
DOI
10.2991/eia-17.2017.41How 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  - Shufen LIU
AU  - Ji HOU
AU  - Lu HAN
PY  - 2017/07
DA  - 2017/07
TI  - A Study on Dispersion Law of Underwater Threat based on Monte Carlo Algorithm
BT  - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
PB  - Atlantis Press
SP  - 191
EP  - 193
SN  - 1951-6851
UR  - https://doi.org/10.2991/eia-17.2017.41
DO  - 10.2991/eia-17.2017.41
ID  - LIU2017/07
ER  -