A robust algorithm for adaptive FIR filtering and its performance analysis with additive contaminated-Gaussian noise

1996 ◽  
Vol 43 (5) ◽  
pp. 361 ◽  
Author(s):  
Seung Chan Bang ◽  
Souguil Ann
Keyword(s):  
Performance Analysis ◽  
Gaussian Noise ◽  
Robust Algorithm ◽  
Fir Filtering ◽  
Adaptive Fir

Performance Analysis of a Robust Wavelet Threshold for Heavy-Tailed Noises

2010 ◽  
Vol 40-41 ◽  
pp. 979-984
Author(s):  
Guang Fen Wei ◽  
Feng Su ◽  
Tao Jian
Keyword(s):  
Performance Analysis ◽  
Gaussian Noise ◽  
Normal Family ◽  
Soft Threshold ◽  
Heavy Tailed ◽  
Threshold Technique

The interesting signal is often contaminated by heavy-tailed noise that has more outliers than Gaussian noise. A robust wavelet threshold based on the minimax description length principle is derived in the ε-contaminated normal family for maximizing the entropy. Compared with classical threshold based on Gaussian assumption, the robust threshold can eliminate the heavy-tailed noise better, even if the precise value of ε is unknown, which shows its robustness. The further experiment shows that soft threshold is more suitable than hard threshold for robust wavelet threshold technique.

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