A Threat Assessment Method for Unmanned Aerial Vehicle Based on Bayesian Networks under the Condition of Small Data Sets. (13th June 2018)
- Record Type:
- Journal Article
- Title:
- A Threat Assessment Method for Unmanned Aerial Vehicle Based on Bayesian Networks under the Condition of Small Data Sets. (13th June 2018)
- Main Title:
- A Threat Assessment Method for Unmanned Aerial Vehicle Based on Bayesian Networks under the Condition of Small Data Sets
- Authors:
- Di, Ruohai
Gao, Xiaoguang
Guo, Zhigao
Wan, Kaifang - Other Names:
- Chicano Francisco Academic Editor.
- Abstract:
- Abstract : The autonomous decision-making of a UAV is based on rapid and accurate threat assessment of the target. Accordingly, modeling of threat assessment under the condition of a small data set is studied in this paper. First, the operational scenario of a manned/unmanned aerial vehicle is constructed, and feature selection and data preprocessing are performed. Second, to obtain the structure, a modeling method for threat assessment is proposed based on an improved BIC score. Finally, the obtained model is applied to compute the threat probability using the junction tree algorithm. The experimental results show that the method proposed in this paper is an available method for threat assessment under the condition of small data sets.
- Is Part Of:
- Mathematical problems in engineering. Volume 2018(2018)
- Journal:
- Mathematical problems in engineering
- Issue:
- Volume 2018(2018)
- Issue Display:
- Volume 2018, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 2018
- Issue:
- 2018
- Issue Sort Value:
- 2018-2018-2018-0000
- Page Start:
- Page End:
- Publication Date:
- 2018-06-13
- Subjects:
- Engineering mathematics -- Periodicals
510.2462 - Journal URLs:
- https://www.hindawi.com/journals/mpe/ ↗
http://www.gbhap-us.com/journals/238/238-top.htm ↗ - DOI:
- 10.1155/2018/8484358 ↗
- Languages:
- English
- ISSNs:
- 1024-123X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 23546.xml