Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet
Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of dete...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10113310/ |
| _version_ | 1797805954229600256 |
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| author | Ziyi Chen Yaowu Shi Yingwei Wang Xinbo Li Xiaohui Yu Yiran Shi |
| author_facet | Ziyi Chen Yaowu Shi Yingwei Wang Xinbo Li Xiaohui Yu Yiran Shi |
| author_sort | Ziyi Chen |
| collection | DOAJ |
| description | Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop’s shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments. |
| first_indexed | 2024-03-13T06:00:23Z |
| format | Article |
| id | doaj.art-a93c8439d73f4c5794d24fa44080ac2f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:00:23Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a93c8439d73f4c5794d24fa44080ac2f2023-06-12T23:01:04ZengIEEEIEEE Access2169-35362023-01-0111460044601410.1109/ACCESS.2023.327172010113310Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNetZiyi Chen0https://orcid.org/0000-0003-2193-9204Yaowu Shi1Yingwei Wang2Xinbo Li3https://orcid.org/0000-0002-1628-1033Xiaohui Yu4https://orcid.org/0000-0001-5986-5306Yiran Shi5https://orcid.org/0000-0003-0846-4267College of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaAccurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop’s shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments.https://ieeexplore.ieee.org/document/10113310/FH signalTFACenterNetcomplex EMIimage detection |
| spellingShingle | Ziyi Chen Yaowu Shi Yingwei Wang Xinbo Li Xiaohui Yu Yiran Shi Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet IEEE Access FH signal TFA CenterNet complex EMI image detection |
| title | Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet |
| title_full | Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet |
| title_fullStr | Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet |
| title_full_unstemmed | Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet |
| title_short | Unlocking Signal Processing With Image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet |
| title_sort | unlocking signal processing with image detection a frequency hopping detection scheme for complex emi environments using stft and centernet |
| topic | FH signal TFA CenterNet complex EMI image detection |
| url | https://ieeexplore.ieee.org/document/10113310/ |
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