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

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	421-424
Number of pages	4
ISBN (Electronic)	9798331574208
DOIs	https://doi.org/10.1109/EEI67650.2025.11334924
Publication status	Published - 2025
Externally published	Yes
Event	7th International Conference on Electronic Engineering and Informatics, EEI 2025 - Yangzhou, China Duration: 7 Nov 2025 → 9 Nov 2025

Publication series

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

Conference

Conference	7th International Conference on Electronic Engineering and Informatics, EEI 2025
Country/Territory	China
City	Yangzhou
Period	7/11/25 → 9/11/25

Keywords

constant false alarm rate
convolutional neural network
multi-target

Access to Document

10.1109/EEI67650.2025.11334924

Cite this

Ni, L., Feng, Y., Zhao, J., & Shan, T. (2025). Constant False Alarm Rate Detection Method Based on Convolutional Neural Networks. In 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025 (pp. 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 et al. / 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. pp. 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. in 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., pp. 421-424, 7th International Conference on Electronic Engineering and Informatics, EEI 2025, Yangzhou, China, 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 et al.
2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 421-424 (2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In 2025 7th International Conference on Electronic Engineering and Informatics, EEI 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 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

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Keywords

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