Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network
Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/4687167 |
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| author | Yuan Cao Ying-Xin Kou An Xu Zhi-Fei Xi |
| author_facet | Yuan Cao Ying-Xin Kou An Xu Zhi-Fei Xi |
| author_sort | Yuan Cao |
| collection | DOAJ |
| description | Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats. |
| format | Article |
| id | doaj-art-b1ff3129d66e49e8a397a14d0d0e96d6 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-b1ff3129d66e49e8a397a14d0d0e96d62025-02-03T07:24:24ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742021-01-01202110.1155/2021/46871674687167Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural NetworkYuan Cao0Ying-Xin Kou1An Xu2Zhi-Fei Xi3Aviation Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAviation Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAviation Engineering College, Air Force Engineering University, Xi’an 710038, ChinaAviation Engineering College, Air Force Engineering University, Xi’an 710038, ChinaTarget threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.http://dx.doi.org/10.1155/2021/4687167 |
| spellingShingle | Yuan Cao Ying-Xin Kou An Xu Zhi-Fei Xi Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network International Journal of Aerospace Engineering |
| title | Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network |
| title_full | Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network |
| title_fullStr | Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network |
| title_full_unstemmed | Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network |
| title_short | Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network |
| title_sort | target threat assessment in air combat based on improved glowworm swarm optimization and elm neural network |
| url | http://dx.doi.org/10.1155/2021/4687167 |
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