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A Data-Driven High-Resolution Time-Frequency Distribution
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AI
DOI：10.1109/LSP.2022.3186228.png)
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Abstract
En 中文
The design of high-resolution and cross-term (CT) free time-frequency distributions (TFDs) has been an open problem. Classical kernel based methods are limited by the trade-off between resolution and CT suppression, even under optimally derived parameters. To break the current limitation, we propose a data-driven model directly based on Wigner-Ville distribution (WVD). The proposed data-driven high-resolution TFD (DH-TFD) includes several stacked multi-channel convolutional kernels. Specifically, convolutional layers with skipping operators are utilized to learn coarse features, while a weighted block is employed to refine these features independently in both channel and spatial dimensions. By doing so, CTs can be effectively eliminated while maintaining a high resolution. Numerical experiments on both synthetic and real-world data confirm the superiority of the proposed DH-TFD in simultaneously extracting and representing a target signal over state-of-the-art methods.
Keywords:
Kernel
Convolution
Signal resolution
Feature extraction
Signal to noise ratio
Training
Time-frequency analysis
Data-driven
kernel function
high-resolution time-frequency distribution
Journal
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