High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals
Frequency-crossing signals are widely found in nature and various engineering systems. Currently, achieving high-resolution time-frequency (TF) representation and accurate instantaneous frequency (IF) estimation for these signals presents a challenge and is a significant area of research. This paper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2030 |
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| author | Hui Li Xiangxiang Zhu Yingfei Wang Xinpeng Cai Zhuosheng Zhang |
| author_facet | Hui Li Xiangxiang Zhu Yingfei Wang Xinpeng Cai Zhuosheng Zhang |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Frequency-crossing signals are widely found in nature and various engineering systems. Currently, achieving high-resolution time-frequency (TF) representation and accurate instantaneous frequency (IF) estimation for these signals presents a challenge and is a significant area of research. This paper proposes a solution that includes a high-concentration TF representation network and an IF separation and estimation network, designed specifically for analyzing frequency-crossing signals using classical TF analysis and U-net techniques. Through TF data generation, the construction of a U-net, and training, the high-concentration TF representation network achieves high-resolution TF characterization of different frequency-crossing signals. The IF separation and estimation network, with its discriminant model, offers flexibility in determining the number of components within multi-component signals. Following this, the separation network model, with an equal number of components, is utilized for signal separation and IF estimation. Finally, a comparison is performed against the short-time Fourier transform, synchrosqueezing transform, and convolutional neural network. Experimental validation shows that our proposed approach achieves high TF concentration, exhibiting robust noise immunity and enabling precise characterization of the time-varying law of frequency-crossing signals. |
| format | Article |
| id | doaj-art-991dac0a68f844bd96315d0fba1e4f19 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-991dac0a68f844bd96315d0fba1e4f192025-08-20T02:09:22ZengMDPI AGSensors1424-82202025-03-01257203010.3390/s25072030High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing SignalsHui Li0Xiangxiang Zhu1Yingfei Wang2Xinpeng Cai3Zhuosheng Zhang4School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mathematics and Statistics, MOE Key Laboratory for Complexity Science in Aerospace, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mathematics and Statistics, MOE Key Laboratory for Complexity Science in Aerospace, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Mathematics and Statistics, MOE Key Laboratory for Complexity Science in Aerospace, Northwestern Polytechnical University, Xi’an 710072, ChinaShenzhen University of Advanced Technology, Shenzhen 518106, ChinaFrequency-crossing signals are widely found in nature and various engineering systems. Currently, achieving high-resolution time-frequency (TF) representation and accurate instantaneous frequency (IF) estimation for these signals presents a challenge and is a significant area of research. This paper proposes a solution that includes a high-concentration TF representation network and an IF separation and estimation network, designed specifically for analyzing frequency-crossing signals using classical TF analysis and U-net techniques. Through TF data generation, the construction of a U-net, and training, the high-concentration TF representation network achieves high-resolution TF characterization of different frequency-crossing signals. The IF separation and estimation network, with its discriminant model, offers flexibility in determining the number of components within multi-component signals. Following this, the separation network model, with an equal number of components, is utilized for signal separation and IF estimation. Finally, a comparison is performed against the short-time Fourier transform, synchrosqueezing transform, and convolutional neural network. Experimental validation shows that our proposed approach achieves high TF concentration, exhibiting robust noise immunity and enabling precise characterization of the time-varying law of frequency-crossing signals.https://www.mdpi.com/1424-8220/25/7/2030time-frequency analysisinstantaneous frequency estimationfully convolutional networkcross-over instantaneous frequency |
| spellingShingle | Hui Li Xiangxiang Zhu Yingfei Wang Xinpeng Cai Zhuosheng Zhang High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals Sensors time-frequency analysis instantaneous frequency estimation fully convolutional network cross-over instantaneous frequency |
| title | High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals |
| title_full | High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals |
| title_fullStr | High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals |
| title_full_unstemmed | High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals |
| title_short | High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals |
| title_sort | high concentration time frequency representation and instantaneous frequency estimation of frequency crossing signals |
| topic | time-frequency analysis instantaneous frequency estimation fully convolutional network cross-over instantaneous frequency |
| url | https://www.mdpi.com/1424-8220/25/7/2030 |
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