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Intercept Point Prediction of Ballistic Missile Defense Using Neural Network Learning
- Source :
- International Journal of Aeronautical and Space Sciences. 21:1092-1104
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- This study proposes an algorithm to calculate fast the predicted intercept point (PIP) of the anti-ballistic missile system. A neural network system is trained to learn the motion of the ballistic target to predict the future target position. Then a prediction algorithm iteratively calculates PIP and the launch time of the interceptor. PIP calculation enables the missile to effectively approach to the ballistic target. Computer simulations using simple interceptor models are conducted, demonstrating the usefulness of the proposed PIP determination algorithm. The proposed algorithm significantly reduces the computation time required for target trajectory prediction in real-time.
- Subjects :
- 020301 aerospace & aeronautics
0209 industrial biotechnology
Artificial neural network
Computer science
Computation
Ballistic missile
Aerospace Engineering
ComputerApplications_COMPUTERSINOTHERSYSTEMS
02 engineering and technology
Launch Time
020901 industrial engineering & automation
Missile
0203 mechanical engineering
Control and Systems Engineering
Position (vector)
Control theory
Trajectory
General Materials Science
Point (geometry)
Electrical and Electronic Engineering
Subjects
Details
- ISSN :
- 20932480 and 2093274X
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- International Journal of Aeronautical and Space Sciences
- Accession number :
- edsair.doi...........ca063f7d8e4a7643b597a42782164860