2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array

Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are u...
Ausführliche Beschreibung

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Autor*in:	Liu, Sheng [verfasserIn] Zhao, Jing Zhang, Yu Wu, Decheng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Nested array AVS DOA estimation ESPRIT algorithm

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 41(2021), 2 vom: 17. Sept., Seite 1115-1130
Übergeordnetes Werk:	volume:41 ; year:2021 ; number:2 ; day:17 ; month:09 ; pages:1115-1130

Links:	Volltext

DOI / URN:	10.1007/s00034-021-01831-5

Katalog-ID:	SPR04610335X

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520			\|a Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal.
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allfields	10.1007/s00034-021-01831-5 doi (DE-627)SPR04610335X (SPR)s00034-021-01831-5-e DE-627 ger DE-627 rakwb eng Liu, Sheng verfasserin aut 2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. Nested array (dpeaa)DE-He213 AVS (dpeaa)DE-He213 DOA estimation (dpeaa)DE-He213 ESPRIT algorithm (dpeaa)DE-He213 Zhao, Jing (orcid)0000-0001-7503-5695 aut Zhang, Yu aut Wu, Decheng aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 41(2021), 2 vom: 17. Sept., Seite 1115-1130 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130 https://dx.doi.org/10.1007/s00034-021-01831-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2021 2 17 09 1115-1130
spelling	10.1007/s00034-021-01831-5 doi (DE-627)SPR04610335X (SPR)s00034-021-01831-5-e DE-627 ger DE-627 rakwb eng Liu, Sheng verfasserin aut 2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. Nested array (dpeaa)DE-He213 AVS (dpeaa)DE-He213 DOA estimation (dpeaa)DE-He213 ESPRIT algorithm (dpeaa)DE-He213 Zhao, Jing (orcid)0000-0001-7503-5695 aut Zhang, Yu aut Wu, Decheng aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 41(2021), 2 vom: 17. Sept., Seite 1115-1130 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130 https://dx.doi.org/10.1007/s00034-021-01831-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2021 2 17 09 1115-1130
allfields_unstemmed	10.1007/s00034-021-01831-5 doi (DE-627)SPR04610335X (SPR)s00034-021-01831-5-e DE-627 ger DE-627 rakwb eng Liu, Sheng verfasserin aut 2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. Nested array (dpeaa)DE-He213 AVS (dpeaa)DE-He213 DOA estimation (dpeaa)DE-He213 ESPRIT algorithm (dpeaa)DE-He213 Zhao, Jing (orcid)0000-0001-7503-5695 aut Zhang, Yu aut Wu, Decheng aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 41(2021), 2 vom: 17. Sept., Seite 1115-1130 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130 https://dx.doi.org/10.1007/s00034-021-01831-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2021 2 17 09 1115-1130
allfieldsGer	10.1007/s00034-021-01831-5 doi (DE-627)SPR04610335X (SPR)s00034-021-01831-5-e DE-627 ger DE-627 rakwb eng Liu, Sheng verfasserin aut 2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. Nested array (dpeaa)DE-He213 AVS (dpeaa)DE-He213 DOA estimation (dpeaa)DE-He213 ESPRIT algorithm (dpeaa)DE-He213 Zhao, Jing (orcid)0000-0001-7503-5695 aut Zhang, Yu aut Wu, Decheng aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 41(2021), 2 vom: 17. Sept., Seite 1115-1130 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130 https://dx.doi.org/10.1007/s00034-021-01831-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2021 2 17 09 1115-1130
allfieldsSound	10.1007/s00034-021-01831-5 doi (DE-627)SPR04610335X (SPR)s00034-021-01831-5-e DE-627 ger DE-627 rakwb eng Liu, Sheng verfasserin aut 2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. Nested array (dpeaa)DE-He213 AVS (dpeaa)DE-He213 DOA estimation (dpeaa)DE-He213 ESPRIT algorithm (dpeaa)DE-He213 Zhao, Jing (orcid)0000-0001-7503-5695 aut Zhang, Yu aut Wu, Decheng aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 41(2021), 2 vom: 17. Sept., Seite 1115-1130 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130 https://dx.doi.org/10.1007/s00034-021-01831-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2021 2 17 09 1115-1130
language	English
source	Enthalten in Circuits, systems and signal processing 41(2021), 2 vom: 17. Sept., Seite 1115-1130 volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130
sourceStr	Enthalten in Circuits, systems and signal processing 41(2021), 2 vom: 17. Sept., Seite 1115-1130 volume:41 year:2021 number:2 day:17 month:09 pages:1115-1130
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nested array AVS DOA estimation ESPRIT algorithm
isfreeaccess_bool	false
container_title	Circuits, systems and signal processing
authorswithroles_txt_mv	Liu, Sheng @@aut@@ Zhao, Jing @@aut@@ Zhang, Yu @@aut@@ Wu, Decheng @@aut@@
publishDateDaySort_date	2021-09-17T00:00:00Z
hierarchy_top_id	351975470
id	SPR04610335X
language_de	englisch
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author	Liu, Sheng
spellingShingle	Liu, Sheng misc Nested array misc AVS misc DOA estimation misc ESPRIT algorithm 2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array
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topic_title	2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array Nested array (dpeaa)DE-He213 AVS (dpeaa)DE-He213 DOA estimation (dpeaa)DE-He213 ESPRIT algorithm (dpeaa)DE-He213
topic	misc Nested array misc AVS misc DOA estimation misc ESPRIT algorithm
topic_unstemmed	misc Nested array misc AVS misc DOA estimation misc ESPRIT algorithm
topic_browse	misc Nested array misc AVS misc DOA estimation misc ESPRIT algorithm
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array
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title_full	2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array
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author_browse	Liu, Sheng Zhao, Jing Zhang, Yu Wu, Decheng
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author-letter	Liu, Sheng
doi_str_mv	10.1007/s00034-021-01831-5
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title_sort	2d doa estimation algorithm by nested acoustic vector-sensor array
title_auth	2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array
abstract	Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract In this paper, a two-dimensional direction-of-arrival (DOA) estimation algorithm based on nested acoustic vector-sensor (AVS) array is introduced. The minimum unit interval of the used nested AVS array is integral multiple of half-wavelength of signal. Firstly, four selection matrices are used to recombine four received vectors, by which four special cross-covariance matrices are obtained to form an extended block covariance matrix. Then, a set of parameters on elevation angles are estimated by traditional estimation of signal parameter via rotational invariance technique (ESPRIT) algorithm. Using these parameters, unambiguous elevation angles can be obtained without spectral peak search and added eigenvalue decomposition. At last, using the estimated elevation angles, the azimuth angles can be estimated by array manifold matching algorithm. The proposed algorithm can distinguish more signals than some existing ESPRIT algorithms. Many simulation results can prove the performance of the proposed algorithm in improving the accuracy of DOA estimation. Simulation results also show that the unit interval of optimal nested AVS array should be larger than half-wavelength of received signal. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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container_issue	2
title_short	2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array
url	https://dx.doi.org/10.1007/s00034-021-01831-5
remote_bool	true
author2	Zhao, Jing Zhang, Yu Wu, Decheng
author2Str	Zhao, Jing Zhang, Yu Wu, Decheng
ppnlink	351975470
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hochschulschrift_bool	false
doi_str	10.1007/s00034-021-01831-5
up_date	2024-07-03T20:22:32.282Z
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2D DOA Estimation Algorithm by Nested Acoustic Vector-Sensor Array

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