DOA estimation based on a deep neural network under impulsive noise

Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits...
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Autor*in:	Cai, Ruiyan [verfasserIn] Tian, Quan Luo, Yang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	DOA estimation Alpha-stable distribution Deep neural network Impulsive noise

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 18(2023), 1 vom: 17. Okt., Seite 785-792
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:1 ; day:17 ; month:10 ; pages:785-792

Links:	Volltext

DOI / URN:	10.1007/s11760-023-02794-7

Katalog-ID:	SPR054523400

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allfields	10.1007/s11760-023-02794-7 doi (DE-627)SPR054523400 (SPR)s11760-023-02794-7-e DE-627 ger DE-627 rakwb eng Cai, Ruiyan verfasserin aut DOA estimation based on a deep neural network under impulsive noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. DOA estimation (dpeaa)DE-He213 Alpha-stable distribution (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Tian, Quan aut Luo, Yang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 17. Okt., Seite 785-792 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:17 month:10 pages:785-792 https://dx.doi.org/10.1007/s11760-023-02794-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 18 2023 1 17 10 785-792
spelling	10.1007/s11760-023-02794-7 doi (DE-627)SPR054523400 (SPR)s11760-023-02794-7-e DE-627 ger DE-627 rakwb eng Cai, Ruiyan verfasserin aut DOA estimation based on a deep neural network under impulsive noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. DOA estimation (dpeaa)DE-He213 Alpha-stable distribution (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Tian, Quan aut Luo, Yang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 17. Okt., Seite 785-792 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:17 month:10 pages:785-792 https://dx.doi.org/10.1007/s11760-023-02794-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 18 2023 1 17 10 785-792
allfields_unstemmed	10.1007/s11760-023-02794-7 doi (DE-627)SPR054523400 (SPR)s11760-023-02794-7-e DE-627 ger DE-627 rakwb eng Cai, Ruiyan verfasserin aut DOA estimation based on a deep neural network under impulsive noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. DOA estimation (dpeaa)DE-He213 Alpha-stable distribution (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Tian, Quan aut Luo, Yang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 17. Okt., Seite 785-792 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:17 month:10 pages:785-792 https://dx.doi.org/10.1007/s11760-023-02794-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 18 2023 1 17 10 785-792
allfieldsGer	10.1007/s11760-023-02794-7 doi (DE-627)SPR054523400 (SPR)s11760-023-02794-7-e DE-627 ger DE-627 rakwb eng Cai, Ruiyan verfasserin aut DOA estimation based on a deep neural network under impulsive noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. DOA estimation (dpeaa)DE-He213 Alpha-stable distribution (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Tian, Quan aut Luo, Yang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 17. Okt., Seite 785-792 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:17 month:10 pages:785-792 https://dx.doi.org/10.1007/s11760-023-02794-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 18 2023 1 17 10 785-792
allfieldsSound	10.1007/s11760-023-02794-7 doi (DE-627)SPR054523400 (SPR)s11760-023-02794-7-e DE-627 ger DE-627 rakwb eng Cai, Ruiyan verfasserin aut DOA estimation based on a deep neural network under impulsive noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. DOA estimation (dpeaa)DE-He213 Alpha-stable distribution (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Tian, Quan aut Luo, Yang aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 17. Okt., Seite 785-792 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:17 month:10 pages:785-792 https://dx.doi.org/10.1007/s11760-023-02794-7 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 18 2023 1 17 10 785-792
language	English
source	Enthalten in Signal, image and video processing 18(2023), 1 vom: 17. Okt., Seite 785-792 volume:18 year:2023 number:1 day:17 month:10 pages:785-792
sourceStr	Enthalten in Signal, image and video processing 18(2023), 1 vom: 17. Okt., Seite 785-792 volume:18 year:2023 number:1 day:17 month:10 pages:785-792
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	DOA estimation Alpha-stable distribution Deep neural network Impulsive noise
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Cai, Ruiyan @@aut@@ Tian, Quan @@aut@@ Luo, Yang @@aut@@
publishDateDaySort_date	2023-10-17T00:00:00Z
hierarchy_top_id	546899102
id	SPR054523400
language_de	englisch
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author	Cai, Ruiyan
spellingShingle	Cai, Ruiyan misc DOA estimation misc Alpha-stable distribution misc Deep neural network misc Impulsive noise DOA estimation based on a deep neural network under impulsive noise
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topic_title	DOA estimation based on a deep neural network under impulsive noise DOA estimation (dpeaa)DE-He213 Alpha-stable distribution (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213
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title	DOA estimation based on a deep neural network under impulsive noise
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title_full	DOA estimation based on a deep neural network under impulsive noise
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title_sort	doa estimation based on a deep neural network under impulsive noise
title_auth	DOA estimation based on a deep neural network under impulsive noise
abstract	Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	DOA estimation based on a deep neural network under impulsive noise
url	https://dx.doi.org/10.1007/s11760-023-02794-7
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author2	Tian, Quan Luo, Yang
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doi_str	10.1007/s11760-023-02794-7
up_date	2024-07-04T02:01:26.725Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract As an important passive radio monitoring and positioning technology, direction-of-arrival (DOA) estimation has always been a key problem in theoretical research and practice, and many algorithms have been developed under additive Gaussian noise environments. However, when the noise exhibits obvious impulsivity, the performance of these algorithms decreases significantly. To suppress impulsive noise, a new correntropy-based adaptive weight factor is derived. Furthermore, a new multilayer deep neural network (DNN) framework is constructed, and a DNN-based DOA estimator is proposed to realize DOA estimation under impulsive noise environments. Experiments results show that the proposed algorithm is significantly better than the existing algorithms in terms of DOA estimation accuracy and robustness.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DOA estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Alpha-stable distribution</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep neural network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Impulsive noise</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Quan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Yang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Signal, image and video processing</subfield><subfield code="d">London [u.a.] : Springer, 2007</subfield><subfield code="g">18(2023), 1 vom: 17. 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