Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem
Introduction. In practical signal processing and its many applications, researchers and engineers try to find a number of harmonics and their frequencies in a time signal contaminated by noise. In this manuscript we propose a new approach to this problem. Aim. The main goal of this work is to embed...
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Saint Petersburg Electrotechnical University "LETI"
2020-07-01
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| Series: | Известия высших учебных заведений России: Радиоэлектроника |
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| Online Access: | https://re.eltech.ru/jour/article/view/435 |
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| author | D. J. Nicolsky G. S. Tipenko |
| author_facet | D. J. Nicolsky G. S. Tipenko |
| author_sort | D. J. Nicolsky |
| collection | DOAJ |
| description | Introduction. In practical signal processing and its many applications, researchers and engineers try to find a number of harmonics and their frequencies in a time signal contaminated by noise. In this manuscript we propose a new approach to this problem. Aim. The main goal of this work is to embed the original time series into a set of multi-dimensional information vectors and then use shift-invariance properties of the exponentials. The information vectors are cast into a new basis where the exponentials could be separated from each other. Materials and methods. We derive a stable technique based on the singular value decomposition (SVD) of lagcovariance and cross-covariance matrices consisting of covariance coefficients computed for index translated copies of an original time series. For these matrices a generalized eigenvalue problem is solved. Results. The original time series is mapped into the basis of the generalized eigenvectors and then separated into components. The phase portrait of each component is analyzed by a pattern recognition technique to distinguish between the phase portraits related to exponentials constituting the signal and the noise. A component related to the exponential has a regular structure, its phase portrait resembles a unitary circle/arc. Any commonly used method could be then used to evaluate the frequency associated with the exponential. Conclusion. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise. One of the significant benefits of the proposed approach is a way to distinguish false and true frequency estimates by the pattern recognition. Some automatization of the pattern recognition is completed by discarding noise-related components, associated with the eigenvectors that have a modulus less than a certain threshold. |
| format | Article |
| id | doaj-art-3ce9ddcb86264cf4ae34797d359d49ad |
| institution | Kabale University |
| issn | 1993-8985 2658-4794 |
| language | Russian |
| publishDate | 2020-07-01 |
| publisher | Saint Petersburg Electrotechnical University "LETI" |
| record_format | Article |
| series | Известия высших учебных заведений России: Радиоэлектроника |
| spelling | doaj-art-3ce9ddcb86264cf4ae34797d359d49ad2025-08-20T03:57:32ZrusSaint Petersburg Electrotechnical University "LETI"Известия высших учебных заведений России: Радиоэлектроника1993-89852658-47942020-07-0123362410.32603/1993-8985-2020-23-3-6-24339Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval ProblemD. J. Nicolsky0G. S. Tipenko1Geophysical Institute, University of Alaska FairbanksInstitute of Environmental Geoscience Russian Academy of SciencesIntroduction. In practical signal processing and its many applications, researchers and engineers try to find a number of harmonics and their frequencies in a time signal contaminated by noise. In this manuscript we propose a new approach to this problem. Aim. The main goal of this work is to embed the original time series into a set of multi-dimensional information vectors and then use shift-invariance properties of the exponentials. The information vectors are cast into a new basis where the exponentials could be separated from each other. Materials and methods. We derive a stable technique based on the singular value decomposition (SVD) of lagcovariance and cross-covariance matrices consisting of covariance coefficients computed for index translated copies of an original time series. For these matrices a generalized eigenvalue problem is solved. Results. The original time series is mapped into the basis of the generalized eigenvectors and then separated into components. The phase portrait of each component is analyzed by a pattern recognition technique to distinguish between the phase portraits related to exponentials constituting the signal and the noise. A component related to the exponential has a regular structure, its phase portrait resembles a unitary circle/arc. Any commonly used method could be then used to evaluate the frequency associated with the exponential. Conclusion. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise. One of the significant benefits of the proposed approach is a way to distinguish false and true frequency estimates by the pattern recognition. Some automatization of the pattern recognition is completed by discarding noise-related components, associated with the eigenvectors that have a modulus less than a certain threshold.https://re.eltech.ru/jour/article/view/435exponential retrieval problemmatrix pencilsvdpattern recognition |
| spellingShingle | D. J. Nicolsky G. S. Tipenko Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem Известия высших учебных заведений России: Радиоэлектроника exponential retrieval problem matrix pencil svd pattern recognition |
| title | Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem |
| title_full | Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem |
| title_fullStr | Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem |
| title_full_unstemmed | Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem |
| title_short | Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem |
| title_sort | application of the non hermitian singular spectrum analysis to the exponential retrieval problem |
| topic | exponential retrieval problem matrix pencil svd pattern recognition |
| url | https://re.eltech.ru/jour/article/view/435 |
| work_keys_str_mv | AT djnicolsky applicationofthenonhermitiansingularspectrumanalysistotheexponentialretrievalproblem AT gstipenko applicationofthenonhermitiansingularspectrumanalysistotheexponentialretrievalproblem |