An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection
In recent years, the development of multi-sensor has emphasized the need to directly process multi-channel (multivariate) data. In this paper, a novel multivariate synchrosqueezing wavelet transform denoising method combined with subspace projection (SWT-SP) is proposed. One of the key points of thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9136715/ |
| _version_ | 1818428856295489536 |
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| author | Peipei Cao Huali Wang Kaijie Zhou |
| author_facet | Peipei Cao Huali Wang Kaijie Zhou |
| author_sort | Peipei Cao |
| collection | DOAJ |
| description | In recent years, the development of multi-sensor has emphasized the need to directly process multi-channel (multivariate) data. In this paper, a novel multivariate synchrosqueezing wavelet transform denoising method combined with subspace projection (SWT-SP) is proposed. One of the key points of this method is to obtain an optimal orthogonal matrix which can project a multivariate observation signal to a signal subspace occupied by a clean signal and an orthogonal noise subspace occupied by noise. Furthermore, the high dimensional time-frequency representation based on the synchrosqueezing transform realizes the multichannel signal information fusion, and the subspace projection makes full use of the spatial diversity characteristics of the observed signal. Finally, signal energy produces the aggregation effect in the former dimension space, which improves the signal-to-noise ratio(SNR) of signals in the signal subspace. The performance of this algorithm for standard multichannel denoising is verified on both real-world data and synthetic signals. The reconstructed signal obtained the improvement of the highest SNR by about 6 dB under different conditions. |
| first_indexed | 2024-12-14T15:08:16Z |
| format | Article |
| id | doaj.art-cbb0fc74aa254c499d0d2cda19fe93cf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:08:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cbb0fc74aa254c499d0d2cda19fe93cf2022-12-21T22:56:39ZengIEEEIEEE Access2169-35362020-01-01812617812618510.1109/ACCESS.2020.30079339136715An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace ProjectionPeipei Cao0https://orcid.org/0000-0001-5258-7171Huali Wang1Kaijie Zhou2School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaInstitute of Communications Engineering, PLA Army Engineering University, Nanjing, ChinaCollege of Physics and Electrical Engineering, Huaiyin Normal University, Huai’an, ChinaIn recent years, the development of multi-sensor has emphasized the need to directly process multi-channel (multivariate) data. In this paper, a novel multivariate synchrosqueezing wavelet transform denoising method combined with subspace projection (SWT-SP) is proposed. One of the key points of this method is to obtain an optimal orthogonal matrix which can project a multivariate observation signal to a signal subspace occupied by a clean signal and an orthogonal noise subspace occupied by noise. Furthermore, the high dimensional time-frequency representation based on the synchrosqueezing transform realizes the multichannel signal information fusion, and the subspace projection makes full use of the spatial diversity characteristics of the observed signal. Finally, signal energy produces the aggregation effect in the former dimension space, which improves the signal-to-noise ratio(SNR) of signals in the signal subspace. The performance of this algorithm for standard multichannel denoising is verified on both real-world data and synthetic signals. The reconstructed signal obtained the improvement of the highest SNR by about 6 dB under different conditions.https://ieeexplore.ieee.org/document/9136715/Subspace projectionsynchrosqueezing transformsignal-to-noise ratio |
| spellingShingle | Peipei Cao Huali Wang Kaijie Zhou An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection IEEE Access Subspace projection synchrosqueezing transform signal-to-noise ratio |
| title | An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection |
| title_full | An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection |
| title_fullStr | An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection |
| title_full_unstemmed | An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection |
| title_short | An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection |
| title_sort | improved multivariate synchrosqueezing wavelet transform denoising method using subspace projection |
| topic | Subspace projection synchrosqueezing transform signal-to-noise ratio |
| url | https://ieeexplore.ieee.org/document/9136715/ |
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