Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks

Lijing Ni; Yuan Feng; Juan Zhao; Tao Shan

doi:10.1109/EEI67650.2025.11334924

Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks

Lijing Ni^*
, Yuan Feng
, Juan Zhao
, Tao Shan

^*此作品的通讯作者

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

This paper proposes an improved variability index constant false alarm rate detector utilizing convolutional neural networks. By combining statistical measures and neural networks to jointly determine different environment and improve strategies for various environments, the proposed method enhances the detection performance of variability index constant false alarm rate detector in multi-target environments. Simulation results demonstrate that the proposed method achieves a higher probability of accurate environmental classification, while maintaining robust detection performance in various scenarios, significantly enhancing the algorithm's adaptability in complex environments.

源语言	英语
主期刊名	2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025
出版商	Institute of Electrical and Electronics Engineers Inc.
页	421-424
页数	4
ISBN（电子版）	9798331574208
DOI	https://doi.org/10.1109/EEI67650.2025.11334924
出版状态	已出版 - 2025
已对外发布	是
活动	7th International Conference on Electronic Engineering and Informatics, EEI 2025 - Yangzhou, 中国期限: 7 11月 2025 → 9 11月 2025

丛书

姓名	2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025

会议

会议	7th International Conference on Electronic Engineering and Informatics, EEI 2025
国家/地区	中国
市	Yangzhou
时期	7/11/25 → 9/11/25

访问文件

10.1109/EEI67650.2025.11334924

其它文件与链接

链接到 Scopus 的出版物

引用此

Ni, L., Feng, Y., Zhao, J., & Shan, T. (2025). Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks. 在 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025 (页码 421-424). (2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEI67650.2025.11334924

Ni, Lijing ; Feng, Yuan ; Zhao, Juan 等. / Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks. 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025. Institute of Electrical and Electronics Engineers Inc., 2025. 页码 421-424 (2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025).

@inproceedings{7a4d69f7129c462280793836a64dfab9,

title = "Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks",

abstract = "This paper proposes an improved variability index constant false alarm rate detector utilizing convolutional neural networks. By combining statistical measures and neural networks to jointly determine different environment and improve strategies for various environments, the proposed method enhances the detection performance of variability index constant false alarm rate detector in multi-target environments. Simulation results demonstrate that the proposed method achieves a higher probability of accurate environmental classification, while maintaining robust detection performance in various scenarios, significantly enhancing the algorithm's adaptability in complex environments.",

keywords = "constant false alarm rate, convolutional neural network, multi-target",

author = "Lijing Ni and Yuan Feng and Juan Zhao and Tao Shan",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 7th International Conference on Electronic Engineering and Informatics, EEI 2025 ; Conference date: 07-11-2025 Through 09-11-2025",

year = "2025",

doi = "10.1109/EEI67650.2025.11334924",

language = "English",

series = "2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "421--424",

booktitle = "2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025",

address = "United States",

}

Ni, L, Feng, Y , Zhao, J & Shan, T 2025, Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks. 在 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025. 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025, Institute of Electrical and Electronics Engineers Inc., 页码 421-424, 7th International Conference on Electronic Engineering and Informatics, EEI 2025, Yangzhou, 中国, 7/11/25. https://doi.org/10.1109/EEI67650.2025.11334924

Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks. / Ni, Lijing; Feng, Yuan ; Zhao, Juan 等.
2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025. Institute of Electrical and Electronics Engineers Inc., 2025. 页码 421-424 (2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks

AU - Ni, Lijing

AU - Feng, Yuan

AU - Zhao, Juan

AU - Shan, Tao

PY - 2025

Y1 - 2025

N2 - This paper proposes an improved variability index constant false alarm rate detector utilizing convolutional neural networks. By combining statistical measures and neural networks to jointly determine different environment and improve strategies for various environments, the proposed method enhances the detection performance of variability index constant false alarm rate detector in multi-target environments. Simulation results demonstrate that the proposed method achieves a higher probability of accurate environmental classification, while maintaining robust detection performance in various scenarios, significantly enhancing the algorithm's adaptability in complex environments.

AB - This paper proposes an improved variability index constant false alarm rate detector utilizing convolutional neural networks. By combining statistical measures and neural networks to jointly determine different environment and improve strategies for various environments, the proposed method enhances the detection performance of variability index constant false alarm rate detector in multi-target environments. Simulation results demonstrate that the proposed method achieves a higher probability of accurate environmental classification, while maintaining robust detection performance in various scenarios, significantly enhancing the algorithm's adaptability in complex environments.

KW - constant false alarm rate

KW - convolutional neural network

KW - multi-target

UR - https://www.scopus.com/pages/publications/105033223266

U2 - 10.1109/EEI67650.2025.11334924

DO - 10.1109/EEI67650.2025.11334924

M3 - Conference contribution

AN - SCOPUS:105033223266

T3 - 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025

SP - 421

EP - 424

BT - 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th International Conference on Electronic Engineering and Informatics, EEI 2025

Y2 - 7 November 2025 through 9 November 2025

ER -

Ni L, Feng Y , Zhao J , Shan T. Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks. 在 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025. Institute of Electrical and Electronics Engineers Inc. 2025. 页码 421-424. (2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025). doi: 10.1109/EEI67650.2025.11334924

Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks

摘要

丛书

会议

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学术指纹

引用此