Ultra-wideband spectrum sensing and carrier frequency estimation for non-cooperative signals based on Nyquist folding receiver and CBDNet-YOLOv5

Yuan Zijie; Qiu Zhaoyang; Wang Pei; Li Bowen; Tang Jiacheng

doi:10.11959/j.issn.1000-0801.2026026

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Ultra-wideband spectrum sensing and carrier frequency estimation for non-cooperative signals based on Nyquist folding receiver and CBDNet-YOLOv5

Research and Development | 更新时间：2026-03-05

- Ultra-wideband spectrum sensing and carrier frequency estimation for non-cooperative signals based on Nyquist folding receiver and CBDNet-YOLOv5
- Telecommunications Science Vol. 42, Issue 2, Pages: 29-44(2026)
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.南京电子设备研究所，江苏南京 210016
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2026026
  CLC： TN971
- Received：29 August 2025，
  
  Revised：2025-08-21，
  
  Accepted：29 August 2025，
  
  Published：20 February 2026
- 稿件说明：
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袁子杰,仇兆炀,王佩等.基于Nyquist折叠接收和CBDNet-YOLOv5的非合作信号超宽带感知与载频测量[J].电信科学,2026,42(02):29-44.

Yuan Zijie,Qiu Zhaoyang,Wang Pei,et al.Ultra-wideband spectrum sensing and carrier frequency estimation for non-cooperative signals based on Nyquist folding receiver and CBDNet-YOLOv5[J].Telecommunications Science,2026,42(02):29-44.
袁子杰,仇兆炀,王佩等.基于Nyquist折叠接收和CBDNet-YOLOv5的非合作信号超宽带感知与载频测量[J].电信科学,2026,42(02):29-44. DOI： 10.11959/j.issn.1000-0801.2026026.

Yuan Zijie,Qiu Zhaoyang,Wang Pei,et al.Ultra-wideband spectrum sensing and carrier frequency estimation for non-cooperative signals based on Nyquist folding receiver and CBDNet-YOLOv5[J].Telecommunications Science,2026,42(02):29-44. DOI： 10.11959/j.issn.1000-0801.2026026.

摘要

随着电磁频谱使用频段的不断拓宽，非合作感知系统的宽带感知能力受到挑战。Nyquist折叠接收机（Nyquist folding receiver，NYFR）具备超宽带感知能力，输出信号重构依赖Nyquist区（Nyquist zone，NZ）标号估计，但是，同时到达多个非合作调制信号时，现有NZ标号估计算法难以具备泛化能力。针对上述问题，提出了基于卷积盲去噪网络（convolutional blind denoising network，CBDNet）和YOLOv5的NYFR输出信号参数估计算法，将NYFR输出信号转换为时频图像，通过CBDNet重构信号时频特征，再利用YOLOv5估计信号NZ标号，最后根据NZ标号重构信号频谱并计算被感知原信号的未知频率。仿真结果表明，该方法能以2 GHz的采样率完成0~20 GHz频段内多种调制信号的频谱感知和载频估计，且在频谱混叠时具有较好的感知性能，提升了感知系统对非合作信号的泛化感知和载频估计能力。

Abstract

With the continuous expansion of the utilized electromagnetic spectrum

the wideband sensing capability of reconnaissance receivers is increasingly challenged. The Nyquist folding receiver (NYFR)

characterized by its ultra-wideband sensing potential

depends on the estimation of the Nyquist zone (NZ) index to reconstruct signal. However

existing NZ index estimation algorithms fail to achieve satisfactory generalization performance for mixed inputs comprising multiple types of non-cooperative signals. Based on the convolutional blind denoising network (CBDNet) and YOLOv5

a novel parameter estimation algorithm for NYFR output signals was proposed to solve these problems. Firstly

the output signals of the NYFR were transformed into time-frequency representations. Then

CBDNet was used to reconstruct the time-frequency features of signal

and YOLOv5 was used to estimate the corresponding NZ index. Based on the estimation results of NZ index

each signal spectrum was reconstructed

and the unknown carrier frequencies of the sensed signals were obtained. Simulation results validate the effectiveness of the algorithm approach

demonstrating its capability to sense spectrum accurately and estimate carrier frequency for a variety of modulation types from 0～20 GHz

under a sampling rate of 2 GHz. Furthermore

the method exhibits robust performance under spectrum aliasing conditions

enhancing the generalization and adaptability of the NYFR for processing non-cooperative modulated signals.

关键词

Keywords

references

吴世鹏 , 孙浩 . 非合作通信中一种高精度载频估计算法 [J ] . 通信技术 , 2021 , 54 ( 4 ): 798 - 802 .

Wu S P , Sun H . A high-precision carrier frequency estimation algorithm for non-cooperative communication [J ] . Communication Technology , 2021 , 54 ( 4 ): 798 - 802 .

肖东 . 基于压缩感知的宽带频谱感知技术研究 [D ] . 哈尔滨 : 哈尔滨工程大学 , 2024 .

Xiao D . Research on wideband spectrum sensing technology based on compressed sensing [D ] . Harbin : Harbin Engineering University , 2024 .

王豪 . 非合作条件下的智能频谱感知技术研究 [D ] . 成都 : 电子科技大学 , 2022 .

Wang H . Research on intelligent spectrum sensing technology under non-cooperative conditions [D ] . Chengdu : University of Electronic Science and Technology of China , 2022 .

Peng X Y , Li J Y , Zhang J , et al . Calibration technique for new-structure, two-channel hybrid filter banks ADC [J ] . IEICE Electronics Express , 2018 , 15 ( 9 ): 20180290 - 20180290 .

崔文涛 , 李杰 , 张德彪 , 等 . 基于国产ADC芯片的TIADC系统时间误差自适应校准算法 [J ] . 仪器仪表学报 , 2021 , 42 ( 11 ): 132 - 139 .

Cui W T , Li J , Zhang D B , et al . TIADC system time error adaptive calibration algorithm based on domestic ADC chip [J ] . Chinese Journal of Scientific Instrument , 2021 , 42 ( 11 ): 132 - 139 .

Liu Y F , Zhao Y , Zhu J , et al . Passive localization algorithm for spaceborne SAR using NYFR and sparse Bayesian learning [J ] . IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences , 2019 , 102 ( 3 ): 581 - 585 .

Wang W , Li Z Y , Qiu Z Y , et al . A parameter estimation method for binary frequency shift keying radar signal intercepted by Nyquist folding receiver [C ] // Proceedings of the IET International Radar Conference . Piscataway : IET Press , 2020 ( 9 ): 1764 - 1768 .

Tian K L , Wan T , Xiong Y , et al . A Nyquist folding receiver architecture with improving Nyquist zone estimation precision [C ] // Proceedings of the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium . Piscataway : IEEE Press , 2022 : 2311 - 2314 .

Liu X Q , Bai J J , Peng J L . Parameter estimation of LFM signal intercepted by synchronous Nyquist folding receiver based on instantaneous autocorrelation [C ] // Proceedings of the 2023 5th International Conference on Electronic Engineering and Informatics (EEI) . Piscataway : IEEE Press , 2023 : 570 - 574 .

李淳夏 . 频率折叠采样接收机局限性分析及改进研究 [D ] . 成都 : 电子科技大学 , 2024 .

Li C X . Limitations analysis and improvement research of frequency folding receiver [D ] . Chengdu : University of Electronic Science and Technology of China , 2024 .

Jiang K L , Tian K L , Feng H C , et al . Distributed UAV swarm augmented wideband spectrum sensing using Nyquist folding receiver [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 10 ): 14171 - 14184 .

高振斌 , 刘进圆 , 宿绍莹 . 基于时频边界拟合的NZ估计优化方法 [J ] . 现代雷达 , 2024 , 46 ( 8 ): 59 - 64 .

Gao Z B , Liu J Y , Su S Y . NZ estimation optimization method based on time-frequency boundary fitting [J ] . Modern Radar , 2024 , 46 ( 8 ): 59 - 64 .

Li C X , Tian K L , Jiang K L , et al . Modeling and optimization of local oscillation signals for Nyquist folding receiver [C ] // Proceedings of the 2023 5th International Conference on Circuits and Systems (ICCS) . Piscataway : IEEE Press , 2023 : 181 - 185 .

Swartz P , Ren S , Sun S . Improved sampling model for the Nyquist folding receiver [C ] // Proceedings of the NAECON 2024-IEEE National Aerospace and Electronics Conference . Piscataway : IEEE Press , 2024 : 173 - 179 .

Murray M J , Schermer R T , Hart J , et al . Deep learning enabled photonic Nyquist folding receiver for wideband RF spectral analysis [J ] . APL Photonics , 2025 , 10 ( 2 ): 026112 .

Guo S , Yan Z F , Zhang K , et al . Toward convolutional blind denoising of real photographs [C ] // Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . Piscataway : IEEE Press , 2019 : 1712 - 1722 .

谈世磊 , 别雄波 , 卢功林 , 等 . 基于YOLOv5网络模型的人员口罩佩戴实时检测 [J ] . 激光杂志 , 2021 , 42 ( 2 ): 147 - 150 .

Tan S L , Bie X B , Lu G L , et al . Real-time detection for mask wearing of personnel based on YOLOv5 network model [J ] . Laser Journal , 2021 , 42 ( 2 ): 147 - 150 .

刘进圆 . 基于NYFR的宽带雷达信号特征提取与参数估计算法研究 [D ] . 天津 : 河北工业大学 , 2023 .

Liu J Y . Research on feature extraction and parameter estimation algorithms of wideband radar signals based on NYFR [D ] . Tianjin : Hebei University of Technology , 2023 .

Qiu Z Y , Li T P , Wang Y M . Parameter estimation of multiple mono‐pulse radar signals intercepted by Nyquist folding receiver using periodic chirp local oscillator based on periodic fractional autocorrelation [J ] . Chinese Journal of Electronics , 2022 , 31 ( 1 ): 181 - 189 .

Jiang K L , Wang D C , Tiang K L , et al . Wideband power spectrum sensing: a fast practical solution for Nyquist folding receiver [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 13 ): 24098 - 24108 .

Wang T , Jiang K L , Ji H , et al . Deep learning-based LPI radar signals analysis and identification using a Nyquist folding receiver architecture [J ] . Defence Technology , 2023 , 19 : 196 - 209 .

刘丽娇 . 机动目标参数估计方法研究 [D ] . 西安 : 西安电子科技大学 , 2014 .

Liu L J . Research on parameter estimation methods for maneuvering targets [D ] . Xi’an : Xidian University , 2014 .

苏汉宁 . 基于深度学习的LFM信号参数估计算法研究 [D ] . 长沙 : 国防科技大学 , 2020 .

Su H N . Research on parameter estimation algorithms of linear frequency modulation signal based on deep learning [D ] . Changsha : National University of Defense Technology , 2020 .

Patrick M J , Field C R , Grae L H L , et al . A comparative analysis of YOLOv8 and U-Net image segmentation approaches for transmission electron micrographs of polycrystalline thin films [J ] . APL Machine Learning , 2025 , 3 ( 3 ): 036105 .

Dhar P , Dutta S , Mukherjee V . Cross-wavelet assisted convolution neural network (AlexNet) approach for phonocardiogram signals classification [J ] . Biomedical Signal Processing and Control , 2021 , 63 : 102142 .

Hou Y T , Wu Z Q , Cai X H , et al . The application of improved DenseNet algorithm in accurate image recognition [J ] . Scientific Reports , 2024 , 14 ( 1 ): 8645 .

Majib M S , Rahman M M , Sazzad T M S , et al . VGG-SCNET: A VGG net-based deep learning framework for brain tumor detection on MRI images [J ] . IEEE Access , 2021 , 9 : 116942 - 116952 .

Gordienko Y , Gordienko N , Taran V , et al . Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning [J ] . Frontiers in Neuroinformatics , 2025 , 19 : 1521805 .

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