Realified L1-PCA for direction-of-arrival estimation: theory and algorithms

Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpect...
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Autor*in:	Markopoulos, Panos P. [verfasserIn] Tsagkarakis, Nicholas [verfasserIn] Pados, Dimitris A. [verfasserIn] Karystinos, George N. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Data contamination Direction-of-arrival estimation Faulty measurements L1 norm L2 norm Multiple signal classification Principal-component analysis Outlier resistance Singular-value decomposition Subspace data processing

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2019(2019), 1 vom: 25. Juni
Übergeordnetes Werk:	volume:2019 ; year:2019 ; number:1 ; day:25 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s13634-019-0625-5

Katalog-ID:	SPR032011091

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520			\|a Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.
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spelling	10.1186/s13634-019-0625-5 doi (DE-627)SPR032011091 (SPR)s13634-019-0625-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Markopoulos, Panos P. verfasserin aut Realified L1-PCA for direction-of-arrival estimation: theory and algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted. Data contamination (dpeaa)DE-He213 Direction-of-arrival estimation (dpeaa)DE-He213 Faulty measurements (dpeaa)DE-He213 L1 norm (dpeaa)DE-He213 L2 norm (dpeaa)DE-He213 Multiple signal classification (dpeaa)DE-He213 Principal-component analysis (dpeaa)DE-He213 Outlier resistance (dpeaa)DE-He213 Singular-value decomposition (dpeaa)DE-He213 Subspace data processing (dpeaa)DE-He213 Tsagkarakis, Nicholas verfasserin aut Pados, Dimitris A. verfasserin aut Karystinos, George N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2019(2019), 1 vom: 25. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2019 year:2019 number:1 day:25 month:06 https://dx.doi.org/10.1186/s13634-019-0625-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2019 2019 1 25 06
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allfieldsGer	10.1186/s13634-019-0625-5 doi (DE-627)SPR032011091 (SPR)s13634-019-0625-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Markopoulos, Panos P. verfasserin aut Realified L1-PCA for direction-of-arrival estimation: theory and algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted. Data contamination (dpeaa)DE-He213 Direction-of-arrival estimation (dpeaa)DE-He213 Faulty measurements (dpeaa)DE-He213 L1 norm (dpeaa)DE-He213 L2 norm (dpeaa)DE-He213 Multiple signal classification (dpeaa)DE-He213 Principal-component analysis (dpeaa)DE-He213 Outlier resistance (dpeaa)DE-He213 Singular-value decomposition (dpeaa)DE-He213 Subspace data processing (dpeaa)DE-He213 Tsagkarakis, Nicholas verfasserin aut Pados, Dimitris A. verfasserin aut Karystinos, George N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2019(2019), 1 vom: 25. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2019 year:2019 number:1 day:25 month:06 https://dx.doi.org/10.1186/s13634-019-0625-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2019 2019 1 25 06
allfieldsSound	10.1186/s13634-019-0625-5 doi (DE-627)SPR032011091 (SPR)s13634-019-0625-5-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Markopoulos, Panos P. verfasserin aut Realified L1-PCA for direction-of-arrival estimation: theory and algorithms 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted. Data contamination (dpeaa)DE-He213 Direction-of-arrival estimation (dpeaa)DE-He213 Faulty measurements (dpeaa)DE-He213 L1 norm (dpeaa)DE-He213 L2 norm (dpeaa)DE-He213 Multiple signal classification (dpeaa)DE-He213 Principal-component analysis (dpeaa)DE-He213 Outlier resistance (dpeaa)DE-He213 Singular-value decomposition (dpeaa)DE-He213 Subspace data processing (dpeaa)DE-He213 Tsagkarakis, Nicholas verfasserin aut Pados, Dimitris A. verfasserin aut Karystinos, George N. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2019(2019), 1 vom: 25. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2019 year:2019 number:1 day:25 month:06 https://dx.doi.org/10.1186/s13634-019-0625-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2019 2019 1 25 06
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2019(2019), 1 vom: 25. Juni volume:2019 year:2019 number:1 day:25 month:06
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topic_facet	Data contamination Direction-of-arrival estimation Faulty measurements L1 norm L2 norm Multiple signal classification Principal-component analysis Outlier resistance Singular-value decomposition Subspace data processing
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Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. 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author	Markopoulos, Panos P.
spellingShingle	Markopoulos, Panos P. ddc 620 bkl 53.73 misc Data contamination misc Direction-of-arrival estimation misc Faulty measurements misc L1 norm misc L2 norm misc Multiple signal classification misc Principal-component analysis misc Outlier resistance misc Singular-value decomposition misc Subspace data processing Realified L1-PCA for direction-of-arrival estimation: theory and algorithms
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topic_title	620 ASE 53.73 bkl Realified L1-PCA for direction-of-arrival estimation: theory and algorithms Data contamination (dpeaa)DE-He213 Direction-of-arrival estimation (dpeaa)DE-He213 Faulty measurements (dpeaa)DE-He213 L1 norm (dpeaa)DE-He213 L2 norm (dpeaa)DE-He213 Multiple signal classification (dpeaa)DE-He213 Principal-component analysis (dpeaa)DE-He213 Outlier resistance (dpeaa)DE-He213 Singular-value decomposition (dpeaa)DE-He213 Subspace data processing (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Data contamination misc Direction-of-arrival estimation misc Faulty measurements misc L1 norm misc L2 norm misc Multiple signal classification misc Principal-component analysis misc Outlier resistance misc Singular-value decomposition misc Subspace data processing
topic_unstemmed	ddc 620 bkl 53.73 misc Data contamination misc Direction-of-arrival estimation misc Faulty measurements misc L1 norm misc L2 norm misc Multiple signal classification misc Principal-component analysis misc Outlier resistance misc Singular-value decomposition misc Subspace data processing
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title	Realified L1-PCA for direction-of-arrival estimation: theory and algorithms
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title_sort	realified l1-pca for direction-of-arrival estimation: theory and algorithms
title_auth	Realified L1-PCA for direction-of-arrival estimation: theory and algorithms
abstract	Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.
abstractGer	Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.
abstract_unstemmed	Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.
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title_short	Realified L1-PCA for direction-of-arrival estimation: theory and algorithms
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Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist among the collected snapshots (e.g., due to unexpected directional jamming). In this work, we present DoA-estimation based on outlier-resistant L1-norm principal component analysis (L1-PCA) of the realified snapshots and a complete algorithmic/theoretical framework for L1-PCA of complex data through realification. Our numerical studies illustrate that the proposed DoA estimation method exhibits (i) similar performance to the conventional L2-PCA-based method, when the processed snapshots are nominal/clean, and (ii) significantly superior performance when the snapshots are faulty/corrupted.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data contamination</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Direction-of-arrival estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Faulty measurements</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">L1 norm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">L2 norm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiple signal classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Principal-component analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Outlier resistance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Singular-value decomposition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Subspace data processing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsagkarakis, Nicholas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pados, Dimitris A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Karystinos, George N.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">EURASIP journal on advances in signal processing</subfield><subfield code="d">Heidelberg : Springer, 2007</subfield><subfield code="g">2019(2019), 1 vom: 25. 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Realified L1-PCA for direction-of-arrival estimation: theory and algorithms

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