Nugyen, YTH, McLernon, D, Ghogho, M orcid.org/0000-0002-0055-7867 et al. (1 more author) (2017) Sparse Reconstruction of Time-Frequency Representation using the Fractional Fourier Transform. In: 2017 International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom). 2017 International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), 09-11 Jan 2016, Da Nang, Vietnam. IEEE , pp. 16-20. ISBN 978-1-5090-2291-5
Abstract
This paper describes a novel method to approximate instantaneous frequency of non-stationary signals through an application of fractional Fourier transform (FRFT). FRFT enables us to build a compact and accurate chirp dictionary for each windowed signal, thus the proposed approach offers improved computational efficiency, and good performance when compared with chirp atom method.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2017, IEEE. This is an author produced version of a paper published in 2017 International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom). Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Chirp, Dictionaries, Time-frequency analysis, Optimized production technology, Atomic measurements, Telecommunications |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Nov 2016 11:18 |
Last Modified: | 18 Jul 2017 11:16 |
Published Version: | https://doi.org/10.1109/SIGTELCOM.2017.7849788 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SIGTELCOM.2017.7849788 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106835 |