Advanced Physical Optics-inspired Support Vector Regression for Efficient Modeling of Target RCS.
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| Title: | Advanced Physical Optics-inspired Support Vector Regression for Efficient Modeling of Target RCS. |
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| Authors: | Chenge Shi1 Chen_geShi7@126.com, Rui Cai1 cairui1201@126.com, Wei Dong1 983683251@qq.com, Donghai Xiao2 xiaodonghai@xidian.edu.cn |
| Source: | Applied Computational Electromagnetics Society Journal. Apr2025, Vol. 40 Issue 4, p309-316. 8p. |
| Subjects: | Radar cross sections, Physical optics, Empirical research, Mathematical optimization, Support vector machines, Data transformations (Statistics), Aerospace engineering |
| Abstract: | This paper proposes an advanced physical optics-inspired support vector regression (APOI-SVR) for efficiently modeling the radar cross section (RCS) of conducting targets. Specifically, an improved physical optics-inspired kernel function is newly proposed by introducing two angular frequency parameters, thereby enhancing the capability of characterizing the various fluctuation patterns in RCS with respect to observation angles. Furthermore, considering the critical role of data preprocessing in facilitating the model's ability to learn the underlying RCS patterns accurately, a physics-based data preprocessing method is introduced. Numerical validations based on two exemplary targets demonstrate that APOI-SVR effectively reduces the predictive root mean square error (RMSE) by over 24.7% compared with the benchmark model. Afterward, APOI-SVR is adopted to quickly establish the RCS feature map of an aircraft model, the results show that it is comparable to numerical simulations in accuracy but less than one-tenth in time cost, indicating the practicality of APOI-SVR for efficiently analyzing the RCS characteristics of targets. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Computational Electromagnetics Society Journal is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187184126 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advanced Physical Optics-inspired Support Vector Regression for Efficient Modeling of Target RCS. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chenge+Shi%22">Chenge Shi</searchLink><relatesTo>1</relatesTo><i> Chen_geShi7@126.com</i><br /><searchLink fieldCode="AR" term="%22Rui+Cai%22">Rui Cai</searchLink><relatesTo>1</relatesTo><i> cairui1201@126.com</i><br /><searchLink fieldCode="AR" term="%22Wei+Dong%22">Wei Dong</searchLink><relatesTo>1</relatesTo><i> 983683251@qq.com</i><br /><searchLink fieldCode="AR" term="%22Donghai+Xiao%22">Donghai Xiao</searchLink><relatesTo>2</relatesTo><i> xiaodonghai@xidian.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Computational+Electromagnetics+Society+Journal%22">Applied Computational Electromagnetics Society Journal</searchLink>. Apr2025, Vol. 40 Issue 4, p309-316. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radar+cross+sections%22">Radar cross sections</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+optics%22">Physical optics</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Data+transformations+%28Statistics%29%22">Data transformations (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Aerospace+engineering%22">Aerospace engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes an advanced physical optics-inspired support vector regression (APOI-SVR) for efficiently modeling the radar cross section (RCS) of conducting targets. Specifically, an improved physical optics-inspired kernel function is newly proposed by introducing two angular frequency parameters, thereby enhancing the capability of characterizing the various fluctuation patterns in RCS with respect to observation angles. Furthermore, considering the critical role of data preprocessing in facilitating the model's ability to learn the underlying RCS patterns accurately, a physics-based data preprocessing method is introduced. Numerical validations based on two exemplary targets demonstrate that APOI-SVR effectively reduces the predictive root mean square error (RMSE) by over 24.7% compared with the benchmark model. Afterward, APOI-SVR is adopted to quickly establish the RCS feature map of an aircraft model, the results show that it is comparable to numerical simulations in accuracy but less than one-tenth in time cost, indicating the practicality of APOI-SVR for efficiently analyzing the RCS characteristics of targets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Computational Electromagnetics Society Journal is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.13052/2025.ACES.J.400404 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 309 Subjects: – SubjectFull: Radar cross sections Type: general – SubjectFull: Physical optics Type: general – SubjectFull: Empirical research Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Data transformations (Statistics) Type: general – SubjectFull: Aerospace engineering Type: general Titles: – TitleFull: Advanced Physical Optics-inspired Support Vector Regression for Efficient Modeling of Target RCS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chenge Shi – PersonEntity: Name: NameFull: Rui Cai – PersonEntity: Name: NameFull: Wei Dong – PersonEntity: Name: NameFull: Donghai Xiao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10544887 Numbering: – Type: volume Value: 40 – Type: issue Value: 4 Titles: – TitleFull: Applied Computational Electromagnetics Society Journal Type: main |
| ResultId | 1 |
