Abstract
Existing algorithms for blind source separation are often based on the eigendecomposition of fourth-order cumulant matrices. However, when the cumulant matrices have close eigenvalues, their eigenvectors are very sensitive to errors in the estimation of the matrices. In this paper, we show how to produce a cumulant matrix that has a well-separated extremal eigenvalue. The corresponding eigenvector is thus well conditioned and can be used to develop robust algorithms for blind source extraction. Some numerical experiments are provided to illustrate the effectiveness of the proposed approach.
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.
