Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection
Existing models for target detection on unmanned aerial vehicle platforms largely rely on the Boolean perception model. However, aiming at electromagnetic radiation sources, the Boolean perception model fails to accurately reflect the propagation and detection characteristics of electromagnetic wave...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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EDP Sciences
2024-12-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1144/jnwpu2024426p1144.html |
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| _version_ | 1825206549461598208 |
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| author | DENG Wenjie CHEN Song WU Di LIU Kaiyue XU Ziliang HE Runze |
| author_facet | DENG Wenjie CHEN Song WU Di LIU Kaiyue XU Ziliang HE Runze |
| author_sort | DENG Wenjie |
| collection | DOAJ |
| description | Existing models for target detection on unmanned aerial vehicle platforms largely rely on the Boolean perception model. However, aiming at electromagnetic radiation sources, the Boolean perception model fails to accurately reflect the propagation and detection characteristics of electromagnetic wave signals. To address this issue, this paper, grounded in the Bayesian signal detection theory and utilizing the distributional characteristics of antenna pattern diagrams, constructs an electromagnetic detection model for unmanned aerial vehicle platforms. The performance of the novel model is derived and analyzed under a scanning coverage search algorithm. Analytical and simulation results indicate that the proposed model effectively describes the process of electromagnetic target detection. In coverage search tasks, when the unmanned aerial vehicle is equipped with the KSHA-BJ32-10-NH antenna, the new model has a shorter range compared to the Boolean perception model, and the advantage of the new model becomes more pronounced as the search area expands. The increase in the required range for the unmanned aerial vehicle to complete the task is only 85.41% of that of the traditional Boolean model. |
| format | Article |
| id | doaj-art-263b2ccfa1544a29bd1f186bb7491c0f |
| institution | Kabale University |
| issn | 1000-2758 2609-7125 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj-art-263b2ccfa1544a29bd1f186bb7491c0f2025-02-07T08:23:13ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252024-12-014261144115110.1051/jnwpu/20244261144jnwpu2024426p1144Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detectionDENG Wenjie0CHEN Song1WU Di2LIU Kaiyue3XU Ziliang4HE Runze5PLA Information Engineering UniversityPLA Information Engineering UniversityPLA Information Engineering UniversityPLA Information Engineering UniversityPLA Information Engineering UniversityPLA Information Engineering UniversityExisting models for target detection on unmanned aerial vehicle platforms largely rely on the Boolean perception model. However, aiming at electromagnetic radiation sources, the Boolean perception model fails to accurately reflect the propagation and detection characteristics of electromagnetic wave signals. To address this issue, this paper, grounded in the Bayesian signal detection theory and utilizing the distributional characteristics of antenna pattern diagrams, constructs an electromagnetic detection model for unmanned aerial vehicle platforms. The performance of the novel model is derived and analyzed under a scanning coverage search algorithm. Analytical and simulation results indicate that the proposed model effectively describes the process of electromagnetic target detection. In coverage search tasks, when the unmanned aerial vehicle is equipped with the KSHA-BJ32-10-NH antenna, the new model has a shorter range compared to the Boolean perception model, and the advantage of the new model becomes more pronounced as the search area expands. The increase in the required range for the unmanned aerial vehicle to complete the task is only 85.41% of that of the traditional Boolean model.https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1144/jnwpu2024426p1144.htmlunmanned aerial vehicleelectromagnetic radiation sourcestarget detectionbayesian signal detection |
| spellingShingle | DENG Wenjie CHEN Song WU Di LIU Kaiyue XU Ziliang HE Runze Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection Xibei Gongye Daxue Xuebao unmanned aerial vehicle electromagnetic radiation sources target detection bayesian signal detection |
| title | Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection |
| title_full | Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection |
| title_fullStr | Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection |
| title_full_unstemmed | Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection |
| title_short | Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection |
| title_sort | electromagnetic target detection model for unmanned aerial vehicle platforms based on bayesian detection |
| topic | unmanned aerial vehicle electromagnetic radiation sources target detection bayesian signal detection |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1144/jnwpu2024426p1144.html |
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