@inproceedings{fa567c14cdd74a29a1d7b63b5d122c40,
title = "Underwater Target Threat Assessment Method Based on Bayesian Network",
abstract = "Underwater targets are used more and more frequently in modern ocean battlefield. It is important to assess the threat from an enemy target after it is detected immediately, in order to prepare quick counterattack and defense. This paper proposes a method for underwater target threat assessment based on Bayesian Network. The threat model uses a hierarchy view for threat factors. All factors are divided into three parts: environment, target space and target fire. Each part contains several detailed factors, which are arranged based on their causal relationships. These factors are applied into the Bayesian Network and the probability of threat is calculated through Bayesian inference. Finally, a simulation is conducted and the method can effectively assess the target threat. The model has a relatively good performance and can generate the assessment with low time cost.",
keywords = "Bayesian Network, Belief Propagation Algorithm, Underwater target, situation awareness, threat assessment",
author = "Danyi Li and Meiqin Liu and Senlin Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 Technical Committee on Control Theory, Chinese Association of Automation.; 40th Chinese Control Conference, CCC 2021 ; Conference date: 26-07-2021 Through 28-07-2021",
year = "2021",
month = jul,
day = "26",
doi = "10.23919/CCC52363.2021.9549765",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "3363--3367",
editor = "Chen Peng and Jian Sun",
booktitle = "Proceedings of the 40th Chinese Control Conference, CCC 2021",
}