2-D direction finding using parallel nested arrays with full co-array aperture extension

Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	He, Jin [verfasserIn] Li, Linna [verfasserIn] Shu, Ting [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Array signal processing direction finding nested array difference co-array

Übergeordnetes Werk:	Enthalten in: Signal processing - Amsterdam [u.a.] : Elsevier, 1979, 178
Übergeordnetes Werk:	volume:178

DOI / URN:	10.1016/j.sigpro.2020.107795

Katalog-ID:	ELV004797892

Internformat


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520			\|a Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10.
650		4	\|a Array signal processing
650		4	\|a direction finding
650		4	\|a nested array
650		4	\|a difference co-array
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700	1		\|a Shu, Ting \|e verfasserin \|4 aut
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author_variant	j h jh l l ll t s ts
matchkey_str	hejinlilinnashuting:2020----:dietofnigsnprlensearywtflca
hierarchy_sort_str	2020
bklnumber	53.73
publishDate	2020
allfields	10.1016/j.sigpro.2020.107795 doi (DE-627)ELV004797892 (ELSEVIER)S0165-1684(20)30339-X DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl He, Jin verfasserin (orcid)0000-0001-6929-6354 aut 2-D direction finding using parallel nested arrays with full co-array aperture extension 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10. Array signal processing direction finding nested array difference co-array Li, Linna verfasserin aut Shu, Ting verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 178 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:178 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 178
spelling	10.1016/j.sigpro.2020.107795 doi (DE-627)ELV004797892 (ELSEVIER)S0165-1684(20)30339-X DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl He, Jin verfasserin (orcid)0000-0001-6929-6354 aut 2-D direction finding using parallel nested arrays with full co-array aperture extension 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10. Array signal processing direction finding nested array difference co-array Li, Linna verfasserin aut Shu, Ting verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 178 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:178 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 178
allfields_unstemmed	10.1016/j.sigpro.2020.107795 doi (DE-627)ELV004797892 (ELSEVIER)S0165-1684(20)30339-X DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl He, Jin verfasserin (orcid)0000-0001-6929-6354 aut 2-D direction finding using parallel nested arrays with full co-array aperture extension 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10. Array signal processing direction finding nested array difference co-array Li, Linna verfasserin aut Shu, Ting verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 178 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:178 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 178
allfieldsGer	10.1016/j.sigpro.2020.107795 doi (DE-627)ELV004797892 (ELSEVIER)S0165-1684(20)30339-X DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl He, Jin verfasserin (orcid)0000-0001-6929-6354 aut 2-D direction finding using parallel nested arrays with full co-array aperture extension 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10. Array signal processing direction finding nested array difference co-array Li, Linna verfasserin aut Shu, Ting verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 178 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:178 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 178
allfieldsSound	10.1016/j.sigpro.2020.107795 doi (DE-627)ELV004797892 (ELSEVIER)S0165-1684(20)30339-X DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl He, Jin verfasserin (orcid)0000-0001-6929-6354 aut 2-D direction finding using parallel nested arrays with full co-array aperture extension 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10. Array signal processing direction finding nested array difference co-array Li, Linna verfasserin aut Shu, Ting verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 178 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:178 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 178
language	English
source	Enthalten in Signal processing 178 volume:178
sourceStr	Enthalten in Signal processing 178 volume:178
format_phy_str_mv	Article
bklname	Nachrichtenübertragung
institution	findex.gbv.de
topic_facet	Array signal processing direction finding nested array difference co-array
dewey-raw	004
isfreeaccess_bool	false
container_title	Signal processing
authorswithroles_txt_mv	He, Jin @@aut@@ Li, Linna @@aut@@ Shu, Ting @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	265784166
dewey-sort	14
id	ELV004797892
language_de	englisch
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author	He, Jin
spellingShingle	He, Jin ddc 004 bkl 53.73 misc Array signal processing misc direction finding misc nested array misc difference co-array 2-D direction finding using parallel nested arrays with full co-array aperture extension
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topic_title	004 000 DE-600 53.73 bkl 2-D direction finding using parallel nested arrays with full co-array aperture extension Array signal processing direction finding nested array difference co-array
topic	ddc 004 bkl 53.73 misc Array signal processing misc direction finding misc nested array misc difference co-array
topic_unstemmed	ddc 004 bkl 53.73 misc Array signal processing misc direction finding misc nested array misc difference co-array
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title	2-D direction finding using parallel nested arrays with full co-array aperture extension
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title_full	2-D direction finding using parallel nested arrays with full co-array aperture extension
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title_sort	2-d direction finding using parallel nested arrays with full co-array aperture extension
title_auth	2-D direction finding using parallel nested arrays with full co-array aperture extension
abstract	Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10.
abstractGer	Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10.
abstract_unstemmed	Sparse arrays, such as nested arrays, are able to resolve more sources than sensors because their difference co-arrays can provide O ( L 2 ) degrees-of-freedom (DOFs) with L physical sensors. Most of the existing nested array direction-finding algorithms apply the spatial smoothing technique to realize this DOFs enhancement. One shortcoming of this type of methods is the efficient co-array aperture is reduced in processing the spatial smoothing, resulting in the DOFs are not fully utilized. In this paper, we propose a new approach using two parallel linear nested arrays to contribute full DOFs for two-dimensional direction-finding. To exploit the entire DOFs, we perform the vectorization of multiple fourth-order cumulant matrices and take the average of their co-array covariance matrices, instead of the spatial smoothing of the vectorization of the data covariance matrix. Based on a well-posed identification analysis, we show that the proposed approach can identify the number of sources approximately three times than the algorithms using the spatial smoothing technique. For example, for a two-level parallel nested array of 2 + 2 sensors in each subarray, the maximum number of sources that can be resolved by the proposed approach is 32, whereas, for most of the existing algorithms, the number reduces to 10.
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title_short	2-D direction finding using parallel nested arrays with full co-array aperture extension
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author2	Li, Linna Shu, Ting
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doi_str	10.1016/j.sigpro.2020.107795
up_date	2024-07-07T00:10:59.553Z
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2-D direction finding using parallel nested arrays with full co-array aperture extension

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