A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning
Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in ba...
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MDPI AG
2024-11-01
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| author | Jiaxing Hao Sen Yang Hongmin Gao |
| author_facet | Jiaxing Hao Sen Yang Hongmin Gao |
| author_sort | Jiaxing Hao |
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| description | Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field. |
| format | Article |
| id | doaj-art-bf0978dffab34b0abb518e7710fbb464 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-bf0978dffab34b0abb518e7710fbb4642024-11-26T17:49:39ZengMDPI AGApplied Sciences2076-34172024-11-0114221065210.3390/app142210652A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep LearningJiaxing Hao0Sen Yang1Hongmin Gao2School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaDepartment of UAV Engineering, Army Engineering University, Shijiazhuang 050003, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaElectromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.https://www.mdpi.com/2076-3417/14/22/10652deep learningelectromagnetic characteristicsmulti-parameter optimizationneural network |
| spellingShingle | Jiaxing Hao Sen Yang Hongmin Gao A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning Applied Sciences deep learning electromagnetic characteristics multi-parameter optimization neural network |
| title | A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning |
| title_full | A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning |
| title_fullStr | A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning |
| title_full_unstemmed | A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning |
| title_short | A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning |
| title_sort | multi parameter optimization method for electromagnetic characteristics fitting based on deep learning |
| topic | deep learning electromagnetic characteristics multi-parameter optimization neural network |
| url | https://www.mdpi.com/2076-3417/14/22/10652 |
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