| Autor: |
Hailong Zhang, Lichun Li, Hongyi Pan, Weinian Li, Siyao Tian |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 24, Iss 8, p 2558 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s24082558 |
| Popis: |
The identification of multi-source signals with time-frequency aliasing is a complex problem in wideband signal reception. The traditional method of first separation and identification especially fails due to the significant separation error under underdetermined conditions when the degree of time-frequency aliasing is high. The single-mode recognition method does not need to be separated first. However, the single-mode features contain less signal information, making it challenging to identify time-frequency aliasing signals accurately. To solve the above problems, this article proposes a time-frequency aliasing signal recognition method based on multi-mode fusion (TRMM). This method uses the U-Net network to extract pixel-by-pixel features of the time-frequency and wave-frequency images and then performs weighted fusion. The multimodal feature scores are used as the classification basis to realize the recognition of the time-frequency aliasing signals. When the SNR is 0 dB, the recognition rate of the four-signal aliasing model can reach more than 97.3%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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