Robust Bias Compensation Method for Sparse Normalised Quasi-Newton Least-Mean with Variable-Mixing-Norm Adaptive Filtering
Ying-Ren Chien,
Han-En Hsieh,
Guobing Qian
Abstract:Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address this issue.
We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combi… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.