Anti-UAV High-Performance Computing Early Warning Neural Network Based on PSO Algorithm

In order to effectively solve the problem that the radar detection system is difficult to detect the “low, small, slow” UAV, the high-performance computing early warning neural network is used to recognize the air UAV in real time and extract the target category and image space location information;...

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Bibliographic Details
Published inScientific programming Vol. 2022; pp. 1 - 14
Main Authors Lei, Yang, Yao, Honglei, Jiang, Bo, Tian, Tian, Xing, Peifei
Format Journal Article
LanguageEnglish
Published New York Hindawi 2022
Hindawi Limited
Subjects
Accuracy
Algorithms
Bandwidths
Computation
Controllers
Design
Global optimization
High performance computing
Mathematical models
Neural networks
Radar detection
Systems design
Visual aspects
Visual fields
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Summary:In order to effectively solve the problem that the radar detection system is difficult to detect the “low, small, slow” UAV, the high-performance computing early warning neural network is used to recognize the air UAV in real time and extract the target category and image space location information; the PSO algorithm is used to optimize the parameters of the anti-UAV to ensure that the anti-UAV not only relies on factors but also fully combines the dependence of the visual field factor to quickly obtain the optimal solution through analyzing the high-performance computing early warning neural network in this paper. This algorithm is used to initialize the anti-UAV resources and improve the global optimization capability of the algorithm proposed in this paper. Finally, the experimental results show that the proposed PSO algorithm has better high-performance computing early warning performance to meet the actual needs of network high-performance computing early-warning neural networks.
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/7150128