Highly Accurate and Noise-Robust Phase Delay Estimation using Multitaper Reassignment
(2023) 31st European Signal Processing Conference, EUSIPCO 2023 In European Signal Processing Conference p.1763-1767- Abstract
The recently developed Phase-Scaled Reassignment (PSR) can estimate phase-difference between two oscillating transient signals with high accuracy. However, in low signal-to-noise ratios (SNRs) the performance of commonly applied reassignment techniques is known to deteriorate. In order to reduce variance in low SNR, we propose a multitaper PSR (mtPSR) method for phase-difference estimation between Gaussian transient signals. Three possible variations of this method are investigated and evaluated, mtPSR1, mtPSR2, and mtPSR3. All three variations are shown to outperform state-of-the-art methods and improve estimation accuracy in low SNR. The mtPSR1 is superior in terms of computational efficiency while the mtPSR3 achieves the highest... (More)
The recently developed Phase-Scaled Reassignment (PSR) can estimate phase-difference between two oscillating transient signals with high accuracy. However, in low signal-to-noise ratios (SNRs) the performance of commonly applied reassignment techniques is known to deteriorate. In order to reduce variance in low SNR, we propose a multitaper PSR (mtPSR) method for phase-difference estimation between Gaussian transient signals. Three possible variations of this method are investigated and evaluated, mtPSR1, mtPSR2, and mtPSR3. All three variations are shown to outperform state-of-the-art methods and improve estimation accuracy in low SNR. The mtPSR1 is superior in terms of computational efficiency while the mtPSR3 achieves the highest accuracy. The mtPSR technique is also shown to be robust to model assumptions. An example of phase delay estimates of the electrical signals measured from the brain reveals promising results.
(Less)
- author
- Akesson, Maria LU ; Keding, Oskar LU ; Reinhold, Isabella LU and Sandsten, Maria LU
- organization
-
- Mathematical Statistics
- LTH Profile Area: AI and Digitalization
- LU Profile Area: Light and Materials
- LU Profile Area: Natural and Artificial Cognition
- LTH Profile Area: Nanoscience and Semiconductor Technology
- LTH Profile Area: Engineering Health
- NanoLund: Centre for Nanoscience
- eSSENCE: The e-Science Collaboration
- Statistical Signal Processing Group (research group)
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
- series title
- European Signal Processing Conference
- pages
- 5 pages
- publisher
- European Signal Processing Conference, EUSIPCO
- conference name
- 31st European Signal Processing Conference, EUSIPCO 2023
- conference location
- Helsinki, Finland
- conference dates
- 2023-09-04 - 2023-09-08
- external identifiers
-
- scopus:85178361574
- ISSN
- 2219-5491
- ISBN
- 9789464593600
- DOI
- 10.23919/EUSIPCO58844.2023.10289747
- language
- English
- LU publication?
- yes
- id
- ee4afe6d-95fd-47cc-a879-cfe07ab5f14b
- date added to LUP
- 2024-01-08 10:44:37
- date last changed
- 2024-01-08 10:45:25
@inproceedings{ee4afe6d-95fd-47cc-a879-cfe07ab5f14b, abstract = {{<p>The recently developed Phase-Scaled Reassignment (PSR) can estimate phase-difference between two oscillating transient signals with high accuracy. However, in low signal-to-noise ratios (SNRs) the performance of commonly applied reassignment techniques is known to deteriorate. In order to reduce variance in low SNR, we propose a multitaper PSR (mtPSR) method for phase-difference estimation between Gaussian transient signals. Three possible variations of this method are investigated and evaluated, mtPSR1, mtPSR2, and mtPSR3. All three variations are shown to outperform state-of-the-art methods and improve estimation accuracy in low SNR. The mtPSR1 is superior in terms of computational efficiency while the mtPSR3 achieves the highest accuracy. The mtPSR technique is also shown to be robust to model assumptions. An example of phase delay estimates of the electrical signals measured from the brain reveals promising results.</p>}}, author = {{Akesson, Maria and Keding, Oskar and Reinhold, Isabella and Sandsten, Maria}}, booktitle = {{31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings}}, isbn = {{9789464593600}}, issn = {{2219-5491}}, language = {{eng}}, pages = {{1763--1767}}, publisher = {{European Signal Processing Conference, EUSIPCO}}, series = {{European Signal Processing Conference}}, title = {{Highly Accurate and Noise-Robust Phase Delay Estimation using Multitaper Reassignment}}, url = {{http://dx.doi.org/10.23919/EUSIPCO58844.2023.10289747}}, doi = {{10.23919/EUSIPCO58844.2023.10289747}}, year = {{2023}}, }