An implementation of adaptive filtering, composed of an unsupervised adaptive filter （UAF）, a multi-step forward linear predictor （FLP）, and an unsupervised multi-step adaptive predictor （UMAP）, is built for sup...An implementation of adaptive filtering, composed of an unsupervised adaptive filter （UAF）, a multi-step forward linear predictor （FLP）, and an unsupervised multi-step adaptive predictor （UMAP）, is built for suppressing impulsive noise in unknown circumstances. This filtering scheme, called unsupervised robust adaptive filter （URAF）, possesses a switching structure, which ensures the robustness against impulsive noise. The FLP is used to detect the possible impulsive noise added to the signal, if the signal is ＂impulse-free＂, the filter UAF can estimate the clean sig- nal. If there exists impulsive noise, the impulse corrupted samples are replaced by predicted ones from the FLP, and then the UMAP estimates the clean signal. Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter, and is effective to restrict large disturbance like impulsive noise when the universal filter fails.展开更多
基金supported by the National Science Fund for Distinguished Young Scholars of China (60925011)
文摘An implementation of adaptive filtering, composed of an unsupervised adaptive filter （UAF）, a multi-step forward linear predictor （FLP）, and an unsupervised multi-step adaptive predictor （UMAP）, is built for suppressing impulsive noise in unknown circumstances. This filtering scheme, called unsupervised robust adaptive filter （URAF）, possesses a switching structure, which ensures the robustness against impulsive noise. The FLP is used to detect the possible impulsive noise added to the signal, if the signal is ＂impulse-free＂, the filter UAF can estimate the clean sig- nal. If there exists impulsive noise, the impulse corrupted samples are replaced by predicted ones from the FLP, and then the UMAP estimates the clean signal. Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter, and is effective to restrict large disturbance like impulsive noise when the universal filter fails.
