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
Autor*in: |
Luan, Shengyang [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2021transfer abstract |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
Enthalten in: Modelling SARS-CoV-2 transmission in a UK university setting - Hill, Edward M. ELSEVIER, 2021, a review journal, Orlando, Fla |
---|---|
Übergeordnetes Werk: |
volume:118 ; year:2021 ; pages:0 |
Links: |
---|
DOI / URN: |
10.1016/j.dsp.2021.103214 |
---|
Katalog-ID: |
ELV055509703 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV055509703 | ||
003 | DE-627 | ||
005 | 20230626041747.0 | ||
007 | cr uuu---uuuuu | ||
008 | 220105s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.dsp.2021.103214 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica |
035 | |a (DE-627)ELV055509703 | ||
035 | |a (ELSEVIER)S1051-2004(21)00253-0 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 610 |q VZ |
084 | |a 44.75 |2 bkl | ||
100 | 1 | |a Luan, Shengyang |e verfasserin |4 aut | |
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 |
264 | 1 | |c 2021transfer abstract | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
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 | |
650 | 7 | |a Generalized covariance |2 Elsevier | |
650 | 7 | |a Non-circular signals |2 Elsevier | |
650 | 7 | |a Alpha-stable distribution |2 Elsevier | |
650 | 7 | |a ESPRIT |2 Elsevier | |
700 | 1 | |a Li, Jiayuan |4 oth | |
700 | 1 | |a Gao, Yinrui |4 oth | |
700 | 1 | |a Zhang, Jinfeng |4 oth | |
700 | 1 | |a Qiu, Tianshuang |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Academic Press |a Hill, Edward M. ELSEVIER |t Modelling SARS-CoV-2 transmission in a UK university setting |d 2021 |d a review journal |g Orlando, Fla |w (DE-627)ELV006540295 |
773 | 1 | 8 | |g volume:118 |g year:2021 |g pages:0 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.dsp.2021.103214 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a SSG-OLC-PHA | ||
936 | b | k | |a 44.75 |j Infektionskrankheiten |j parasitäre Krankheiten |x Medizin |q VZ |
951 | |a AR | ||
952 | |d 118 |j 2021 |h 0 |
author_variant |
s l sl |
---|---|
matchkey_str |
luanshengyanglijiayuangaoyinruizhangjinf:2021----:eeaiecvracbsdsrtieouinoietooarvlsiainosrclnniclri |
hierarchy_sort_str |
2021transfer abstract |
bklnumber |
44.75 |
publishDate |
2021 |
allfields |
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 |
spelling |
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 |
allfields_unstemmed |
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 |
allfieldsGer |
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 |
allfieldsSound |
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 |
language |
English |
source |
Enthalten in Modelling SARS-CoV-2 transmission in a UK university setting Orlando, Fla volume:118 year:2021 pages:0 |
sourceStr |
Enthalten in Modelling SARS-CoV-2 transmission in a UK university setting Orlando, Fla volume:118 year:2021 pages:0 |
format_phy_str_mv |
Article |
bklname |
Infektionskrankheiten parasitäre Krankheiten |
institution |
findex.gbv.de |
topic_facet |
Direction of arrival Generalized covariance Non-circular signals Alpha-stable distribution ESPRIT |
dewey-raw |
610 |
isfreeaccess_bool |
false |
container_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
authorswithroles_txt_mv |
Luan, Shengyang @@aut@@ Li, Jiayuan @@oth@@ Gao, Yinrui @@oth@@ Zhang, Jinfeng @@oth@@ Qiu, Tianshuang @@oth@@ |
publishDateDaySort_date |
2021-01-01T00:00:00Z |
hierarchy_top_id |
ELV006540295 |
dewey-sort |
3610 |
id |
ELV055509703 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055509703</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041747.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2021.103214</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055509703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(21)00253-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.75</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Luan, Shengyang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Direction of arrival</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Generalized covariance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-circular signals</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Alpha-stable distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ESPRIT</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jiayuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Yinrui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Jinfeng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qiu, Tianshuang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Hill, Edward M. ELSEVIER</subfield><subfield code="t">Modelling SARS-CoV-2 transmission in a UK university setting</subfield><subfield code="d">2021</subfield><subfield code="d">a review journal</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV006540295</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:118</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.dsp.2021.103214</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.75</subfield><subfield code="j">Infektionskrankheiten</subfield><subfield code="j">parasitäre Krankheiten</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">118</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
author |
Luan, Shengyang |
spellingShingle |
Luan, Shengyang ddc 610 bkl 44.75 Elsevier Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
authorStr |
Luan, Shengyang |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV006540295 |
format |
electronic Article |
dewey-ones |
610 - Medicine & health |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
610 VZ 44.75 bkl Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT Elsevier |
topic |
ddc 610 bkl 44.75 Elsevier Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT |
topic_unstemmed |
ddc 610 bkl 44.75 Elsevier Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT |
topic_browse |
ddc 610 bkl 44.75 Elsevier Direction of arrival Elsevier Generalized covariance Elsevier Non-circular signals Elsevier Alpha-stable distribution Elsevier ESPRIT |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
j l jl y g yg j z jz t q tq |
hierarchy_parent_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
hierarchy_parent_id |
ELV006540295 |
dewey-tens |
610 - Medicine & health |
hierarchy_top_title |
Modelling SARS-CoV-2 transmission in a UK university setting |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV006540295 |
title |
Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
ctrlnum |
(DE-627)ELV055509703 (ELSEVIER)S1051-2004(21)00253-0 |
title_full |
Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
author_sort |
Luan, Shengyang |
journal |
Modelling SARS-CoV-2 transmission in a UK university setting |
journalStr |
Modelling SARS-CoV-2 transmission in a UK university setting |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2021 |
contenttype_str_mv |
zzz |
container_start_page |
0 |
author_browse |
Luan, Shengyang |
container_volume |
118 |
class |
610 VZ 44.75 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Luan, Shengyang |
doi_str_mv |
10.1016/j.dsp.2021.103214 |
dewey-full |
610 |
title_sort |
generalized covariance-based esprit-like solution to direction of arrival estimation for strictly non-circular signals under alpha-stable distributed noise |
title_auth |
Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
abstract |
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. |
abstract_unstemmed |
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. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
title_short |
Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise |
url |
https://doi.org/10.1016/j.dsp.2021.103214 |
remote_bool |
true |
author2 |
Li, Jiayuan Gao, Yinrui Zhang, Jinfeng Qiu, Tianshuang |
author2Str |
Li, Jiayuan Gao, Yinrui Zhang, Jinfeng Qiu, Tianshuang |
ppnlink |
ELV006540295 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth |
doi_str |
10.1016/j.dsp.2021.103214 |
up_date |
2024-07-06T17:44:52.358Z |
_version_ |
1803852597019279360 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055509703</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041747.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dsp.2021.103214</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001542.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055509703</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1051-2004(21)00253-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.75</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Luan, Shengyang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Generalized covariance-based ESPRIT-like solution to direction of arrival estimation for strictly non-circular signals under Alpha-stable distributed noise</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Direction of arrival</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Generalized covariance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Non-circular signals</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Alpha-stable distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ESPRIT</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jiayuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Yinrui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Jinfeng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qiu, Tianshuang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Hill, Edward M. ELSEVIER</subfield><subfield code="t">Modelling SARS-CoV-2 transmission in a UK university setting</subfield><subfield code="d">2021</subfield><subfield code="d">a review journal</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV006540295</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:118</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.dsp.2021.103214</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.75</subfield><subfield code="j">Infektionskrankheiten</subfield><subfield code="j">parasitäre Krankheiten</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">118</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
score |
7.4002113 |