Abstract
The online estimation utilizing the group sparse property is developed in the presence of the impulsive noise. The traditional algorithm, for example, least mean squares (LMS), cannot perform accurate estimation because its cost function is not well defined under the impulsive noise environment. Close inspecting the impulsive noise reveals that the noise is not only sparse because of its impulsive nature, but also has the group sparse property in the time domain since non-zero values occur in cluster. By utilizing this feature, the cost function of LMS is reformulated, and a joint group sparse online estimation approach is developed. The proposed approach explores advantage of this noise structure to better suppress the impulsive noise. Numerical simulations demonstrate that the proposed methods perform well in terms of the estimation accuracy, convergence rate and steady state error than traditional estimation approaches.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
