Polynomial Phase Signal Denoising via Sparse Representations Over a SMAF-Based Dictionary Learning Algorithm
In this paper, we address the problem of denoising polynomial phase signals (PPS) by removing additive white Gaussian noise. Our approach is based on sparse representation using a trained dictionary, which is obtained through the secondary moving average filtering (SMAF) dictionary learning algorith...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10902379/ |
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