Abstract
Traditional quadratic time-frequency distributions are not designed to deal with randomly undersampled signals or data with missing samples. The compressed data measurements introduce noise-like artifacts in the ambiguity domain, compounding the problem of separating the signal auto-terms and cross-terms. In this paper, we propose a multi-task kernel design for suppressing both the artifacts and the cross-terms, while preserving the signal desirable auto-terms. The proposed approach results in highly concentrated time-frequency signature. We evaluate our approach using various polynomial phase signals and show its benefits, especially in the case of strong artifacts.
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.
