A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources
Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the...
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| Autor*in: |
Yin, Kejun [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Springer US, 1982, 41(2022), 11 vom: 06. Juni, Seite 6547-6559 |
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| Übergeordnetes Werk: |
volume:41 ; year:2022 ; number:11 ; day:06 ; month:06 ; pages:6547-6559 |
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10.1007/s00034-022-02065-9 |
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| 520 | |a Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. | ||
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10.1007/s00034-022-02065-9 doi (DE-627)OLC2079570234 (DE-He213)s00034-022-02065-9-p DE-627 ger DE-627 rakwb eng 600 VZ Yin, Kejun verfasserin (orcid)0000-0003-1057-2531 aut A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. Source localization Far-field Near-field MUSIC algorithm Dai, Yun aut Gao, Chunming aut Enthalten in Circuits, systems and signal processing Springer US, 1982 41(2022), 11 vom: 06. Juni, Seite 6547-6559 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:41 year:2022 number:11 day:06 month:06 pages:6547-6559 https://doi.org/10.1007/s00034-022-02065-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 41 2022 11 06 06 6547-6559 |
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10.1007/s00034-022-02065-9 doi (DE-627)OLC2079570234 (DE-He213)s00034-022-02065-9-p DE-627 ger DE-627 rakwb eng 600 VZ Yin, Kejun verfasserin (orcid)0000-0003-1057-2531 aut A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. Source localization Far-field Near-field MUSIC algorithm Dai, Yun aut Gao, Chunming aut Enthalten in Circuits, systems and signal processing Springer US, 1982 41(2022), 11 vom: 06. Juni, Seite 6547-6559 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:41 year:2022 number:11 day:06 month:06 pages:6547-6559 https://doi.org/10.1007/s00034-022-02065-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 41 2022 11 06 06 6547-6559 |
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10.1007/s00034-022-02065-9 doi (DE-627)OLC2079570234 (DE-He213)s00034-022-02065-9-p DE-627 ger DE-627 rakwb eng 600 VZ Yin, Kejun verfasserin (orcid)0000-0003-1057-2531 aut A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. Source localization Far-field Near-field MUSIC algorithm Dai, Yun aut Gao, Chunming aut Enthalten in Circuits, systems and signal processing Springer US, 1982 41(2022), 11 vom: 06. Juni, Seite 6547-6559 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:41 year:2022 number:11 day:06 month:06 pages:6547-6559 https://doi.org/10.1007/s00034-022-02065-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 41 2022 11 06 06 6547-6559 |
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10.1007/s00034-022-02065-9 doi (DE-627)OLC2079570234 (DE-He213)s00034-022-02065-9-p DE-627 ger DE-627 rakwb eng 600 VZ Yin, Kejun verfasserin (orcid)0000-0003-1057-2531 aut A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. Source localization Far-field Near-field MUSIC algorithm Dai, Yun aut Gao, Chunming aut Enthalten in Circuits, systems and signal processing Springer US, 1982 41(2022), 11 vom: 06. Juni, Seite 6547-6559 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:41 year:2022 number:11 day:06 month:06 pages:6547-6559 https://doi.org/10.1007/s00034-022-02065-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 41 2022 11 06 06 6547-6559 |
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10.1007/s00034-022-02065-9 doi (DE-627)OLC2079570234 (DE-He213)s00034-022-02065-9-p DE-627 ger DE-627 rakwb eng 600 VZ Yin, Kejun verfasserin (orcid)0000-0003-1057-2531 aut A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. Source localization Far-field Near-field MUSIC algorithm Dai, Yun aut Gao, Chunming aut Enthalten in Circuits, systems and signal processing Springer US, 1982 41(2022), 11 vom: 06. Juni, Seite 6547-6559 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:41 year:2022 number:11 day:06 month:06 pages:6547-6559 https://doi.org/10.1007/s00034-022-02065-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 41 2022 11 06 06 6547-6559 |
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Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract In this paper, a new multiple signal classification (MUSIC)-based source localization algorithm is proposed to localize a hybrid of near-field (NF) and far-field (FF) sources. The MUSIC estimator for localizing the FF sources is referred to as the FF-MUSIC estimator. On the other hand, the NF-MUSIC estimator is designed for localizing the NF sources as well. In between the processing of these two estimators, the Capon spatial spectrum is exploited to reconstruct a pure NF covariance matrix. The proposed algorithm requires only several one-dimensional (1-D) spectral searches and does not involve any computations of high-dimensional iterative optimization, higher-order cumulants, and parameter pairing. Unlike the existing algorithms, which are derived based on the second-order Taylor series approximation, i.e., Fresnel approximation, of the spatial phase factor, the proposed algorithm is applicable to any-order expansion of the spatial phase factor. Moreover, it works well for non-uniform arrays, whereas the existing algorithms can only be applied to uniform arrays. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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A MUSIC-Based Algorithm For Localization of Hybrid Near-Field and Far-Field Sources |
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