Back to Search
Start Over
Maneuver Decision-Making of Deep Learning for UCAV Thorough Azimuth Angles
- Source :
- IEEE Access, Vol 8, Pp 12976-12987 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Maneuver decision-making directly determines the success or failure of air combat. To improve the dogfight ability of unmanned combat aerial vehicles and avoid the deficiencies of traditional methods, such as poor flexibility and a weak decision-making ability, a maneuver method using deep learning is proposed. A total of 72 different maneuvers are constructed, and 544320 states are designed. Flight simulations are conducted under these different states to obtain corresponding future azimuth angles. A deep neural network is trained with these offline data, and thus, the network possesses state prediction capability. A situation assessment function and a decision objective function based on azimuth angles are constructed. During air combat, the optimal maneuver is selected from the maneuver library according to the predicted state and the decision objective function. The results of air combat simulations indicate that the unmanned combat aerial vehicle (UCAV) can win the air combat game by the proposed method in a balanced situation and can meet missile launching conditions in an adverse situation. The operational time of this method has been reduced by 0.01 s compared with the comparison method.
- Subjects :
- General Computer Science
Computer science
media_common.quotation_subject
Unmanned combat aerial vehicle
situation assessment
ComputerApplications_COMPUTERSINOTHERSYSTEMS
02 engineering and technology
01 natural sciences
Missile
Control theory
General Materials Science
Function (engineering)
media_common
Flexibility (engineering)
air combat simulation
business.industry
Deep learning
010401 analytical chemistry
General Engineering
deep learning
decision-making
021001 nanoscience & nanotechnology
0104 chemical sciences
Azimuth
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
0210 nano-technology
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....7fffcc83f0fa6b822da0ff0a87dd26d5