Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise
Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solut...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Luan, Shengyang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Modelling SARS-CoV-2 transmission in a UK university setting - Hill, Edward M. ELSEVIER, 2021, a review journal, Orlando, Fla |
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| Übergeordnetes Werk: |
volume:118 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.dsp.2021.103214 |
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ELV055509703 |
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| 245 | 1 | 0 | |a Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
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| 520 | |a Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. | ||
| 520 | |a Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. | ||
| 650 | 7 | |a Direction of arrival |2 Elsevier | |
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10.1016/j.dsp.2021.103214 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509703 (ELSEVIER)S1051-2004(21)00253-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Luan, Shengyang verfasserin aut Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Elsevier Li, Jiayuan oth Gao, Yinrui oth Zhang, Jinfeng oth Qiu, Tianshuang oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103214 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
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10.1016/j.dsp.2021.103214 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509703 (ELSEVIER)S1051-2004(21)00253-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Luan, Shengyang verfasserin aut Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Elsevier Li, Jiayuan oth Gao, Yinrui oth Zhang, Jinfeng oth Qiu, Tianshuang oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103214 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
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10.1016/j.dsp.2021.103214 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509703 (ELSEVIER)S1051-2004(21)00253-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Luan, Shengyang verfasserin aut Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Elsevier Li, Jiayuan oth Gao, Yinrui oth Zhang, Jinfeng oth Qiu, Tianshuang oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103214 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
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10.1016/j.dsp.2021.103214 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509703 (ELSEVIER)S1051-2004(21)00253-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Luan, Shengyang verfasserin aut Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Elsevier Li, Jiayuan oth Gao, Yinrui oth Zhang, Jinfeng oth Qiu, Tianshuang oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103214 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
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10.1016/j.dsp.2021.103214 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica (DE-627)ELV055509703 (ELSEVIER)S1051-2004(21)00253-0 DE-627 ger DE-627 rakwb eng 610 VZ 44.75 bkl Luan, Shengyang verfasserin aut Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Elsevier Li, Jiayuan oth Gao, Yinrui oth Zhang, Jinfeng oth Qiu, Tianshuang oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:118 year:2021 pages:0 https://doi.org/10.1016/j.dsp.2021.103214 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 118 2021 0 |
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Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
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Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. |
| abstractGer |
Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. |
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Direction of arrival (DOA) estimation with high resolution and low computational complexity has been one essential research topic. Meanwhile, many solutions have been proposed to handle parameter estimation problems for strictly non-circular (NC) signals. However, second-order statistics-based solutions usually degrade under fractional lower-order Alpha-stable distributed noise. To solve this problem, three concepts are first defined, and then related properties are proved, including a specific generalized covariance (GC), the non-circularity based on GC, and the extended GC matrix. Based on these concepts and theorems, a novel ESPRIT-like method is derived especially for strictly non-circular sources and termed NC-GC-ESPRIT. Monte-Carlo simulations under four different experimental conditions are executed, involving eleven state-of-the-art DOA approaches as comparison candidates. Thus, the superior performance of the proposed solution is validated through the comparison with these candidate algorithms. |
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Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
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