Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data

Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex ellipticall...
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Autor*in:	Fortunati, Stefano [verfasserIn] Gini, Fulvio [verfasserIn] Greco, Maria S. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Covariance matrix estimation Complex elliptically symmetric distribution Detection problem Constrained Cramér-Rao bound Misspecified Cramér-Rao bound

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2016(2016), 1 vom: 21. Nov.
Übergeordnetes Werk:	volume:2016 ; year:2016 ; number:1 ; day:21 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s13634-016-0417-0

Katalog-ID:	SPR032008813

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520			\|a Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.
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allfields	10.1186/s13634-016-0417-0 doi (DE-627)SPR032008813 (SPR)s13634-016-0417-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Fortunati, Stefano verfasserin aut Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector. Covariance matrix estimation (dpeaa)DE-He213 Complex elliptically symmetric distribution (dpeaa)DE-He213 Detection problem (dpeaa)DE-He213 Constrained Cramér-Rao bound (dpeaa)DE-He213 Misspecified Cramér-Rao bound (dpeaa)DE-He213 Gini, Fulvio verfasserin aut Greco, Maria S. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 21. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:21 month:11 https://dx.doi.org/10.1186/s13634-016-0417-0 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 2016 2016 1 21 11
spelling	10.1186/s13634-016-0417-0 doi (DE-627)SPR032008813 (SPR)s13634-016-0417-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Fortunati, Stefano verfasserin aut Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector. Covariance matrix estimation (dpeaa)DE-He213 Complex elliptically symmetric distribution (dpeaa)DE-He213 Detection problem (dpeaa)DE-He213 Constrained Cramér-Rao bound (dpeaa)DE-He213 Misspecified Cramér-Rao bound (dpeaa)DE-He213 Gini, Fulvio verfasserin aut Greco, Maria S. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 21. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:21 month:11 https://dx.doi.org/10.1186/s13634-016-0417-0 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 2016 2016 1 21 11
allfields_unstemmed	10.1186/s13634-016-0417-0 doi (DE-627)SPR032008813 (SPR)s13634-016-0417-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Fortunati, Stefano verfasserin aut Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector. Covariance matrix estimation (dpeaa)DE-He213 Complex elliptically symmetric distribution (dpeaa)DE-He213 Detection problem (dpeaa)DE-He213 Constrained Cramér-Rao bound (dpeaa)DE-He213 Misspecified Cramér-Rao bound (dpeaa)DE-He213 Gini, Fulvio verfasserin aut Greco, Maria S. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 21. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:21 month:11 https://dx.doi.org/10.1186/s13634-016-0417-0 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 2016 2016 1 21 11
allfieldsGer	10.1186/s13634-016-0417-0 doi (DE-627)SPR032008813 (SPR)s13634-016-0417-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Fortunati, Stefano verfasserin aut Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector. Covariance matrix estimation (dpeaa)DE-He213 Complex elliptically symmetric distribution (dpeaa)DE-He213 Detection problem (dpeaa)DE-He213 Constrained Cramér-Rao bound (dpeaa)DE-He213 Misspecified Cramér-Rao bound (dpeaa)DE-He213 Gini, Fulvio verfasserin aut Greco, Maria S. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 21. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:21 month:11 https://dx.doi.org/10.1186/s13634-016-0417-0 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 2016 2016 1 21 11
allfieldsSound	10.1186/s13634-016-0417-0 doi (DE-627)SPR032008813 (SPR)s13634-016-0417-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Fortunati, Stefano verfasserin aut Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector. Covariance matrix estimation (dpeaa)DE-He213 Complex elliptically symmetric distribution (dpeaa)DE-He213 Detection problem (dpeaa)DE-He213 Constrained Cramér-Rao bound (dpeaa)DE-He213 Misspecified Cramér-Rao bound (dpeaa)DE-He213 Gini, Fulvio verfasserin aut Greco, Maria S. verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 21. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:21 month:11 https://dx.doi.org/10.1186/s13634-016-0417-0 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 2016 2016 1 21 11
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2016(2016), 1 vom: 21. Nov. volume:2016 year:2016 number:1 day:21 month:11
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Covariance matrix estimation Complex elliptically symmetric distribution Detection problem Constrained Cramér-Rao bound Misspecified Cramér-Rao bound
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container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Fortunati, Stefano @@aut@@ Gini, Fulvio @@aut@@ Greco, Maria S. @@aut@@
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author	Fortunati, Stefano
spellingShingle	Fortunati, Stefano ddc 620 bkl 53.73 misc Covariance matrix estimation misc Complex elliptically symmetric distribution misc Detection problem misc Constrained Cramér-Rao bound misc Misspecified Cramér-Rao bound Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data
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topic_title	620 ASE 53.73 bkl Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data Covariance matrix estimation (dpeaa)DE-He213 Complex elliptically symmetric distribution (dpeaa)DE-He213 Detection problem (dpeaa)DE-He213 Constrained Cramér-Rao bound (dpeaa)DE-He213 Misspecified Cramér-Rao bound (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Covariance matrix estimation misc Complex elliptically symmetric distribution misc Detection problem misc Constrained Cramér-Rao bound misc Misspecified Cramér-Rao bound
topic_unstemmed	ddc 620 bkl 53.73 misc Covariance matrix estimation misc Complex elliptically symmetric distribution misc Detection problem misc Constrained Cramér-Rao bound misc Misspecified Cramér-Rao bound
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title	Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data
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title_full	Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data
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title_sort	matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data
title_auth	Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data
abstract	Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.
abstractGer	Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.
abstract_unstemmed	Abstract Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.
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title_short	Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data
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Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data

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