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Online Graph Models: Tackling the Challenges of Non-Gaussian Noise in Adaptive Filtering.
This study introduces a novel graph signal processing (GSP) method to effectively remove non-Gaussian noise. The new graph smoothness recursive adaptive filtering (GS-RAF) algorithm enhances adaptive filtering performance in complex noise environments.
Area of Science:
- Signal Processing
- Adaptive Filtering
- Graph Signal Processing (GSP)
Background:
- Adaptive filtering struggles with complex non-Gaussian noise.
- Graph signal processing (GSP) is effective for data with intricate structures.
Purpose of the Study:
- To introduce a novel method for non-Gaussian noise reduction using graph domain perspective.
- To develop an online time-varying graph model and a graph topology transformation strategy.
Main Methods:
- Developed an online time-varying graph model based on filter error signal.
- Introduced a graph topology transformation strategy.
- Defined a new adaptive filtering cost function using graph smoothness and the graph Laplacian matrix.
- Derived the graph smoothness recursive adaptive filtering (GS-RAF) algorithm.
Main Results:
- The GS-RAF algorithm demonstrates theoretical performance analysis.
- Efficacy validated through simulations and echo cancellation experiments.
- MATLAB codes are publicly available for reproducibility.
Conclusions:
- The proposed GSP approach effectively addresses non-Gaussian noise challenges in adaptive filtering.
- The GS-RAF algorithm offers a robust solution for noise reduction in complex signal environments.
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