Identifying of Digital Signals Based on Manifold Learning
| Autor: | Qingbo Ji, Zhiqiang Wu, Zheng Dou, Boyang Feng, Yun Lin, Zhiping Zhang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Threshold limit value
business.industry Gauss Short-time Fourier transform Nonlinear dimensionality reduction 020206 networking & telecommunications Pattern recognition 02 engineering and technology White noise QAM Wavelet Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) Mathematics |
| Zdroj: | International Journal of Signal Processing, Image Processing and Pattern Recognition. 9:127-134 |
| ISSN: | 2005-4254 |
| Popis: | Modulation type is one of the most important characteristics used in signal recognition. An algorithm to realize signal modulation identification is proposed in this paper. We applied wavelet transformation and STFT to the signal, and then used manifold learning method to reduce the high dimension and extracted the recognition feature. The proper threshold value was set as the classifier to achieve the purpose of recognizing 4 kinds of signals (MASK, MFSK, MPSK,QAM) in Gauss white noise environment. The algorithm requires priori signal information no other than signal-to-noise rate. Simulation result indicates the algorithm achieves good performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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