Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals
A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and comp...
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Xia, Yili [verfasserIn] |
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Englisch |
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Enthalten in: IEEE signal processing letters - Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5, New York, NY, 19XX, PP, 99, Seite 1-1 |
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volume:PP ; number:99 ; pages:1-1 |
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10.1109/LSP.2017.2717945 |
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| 520 | |a A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. | ||
| 650 | 4 | |a Covariance matrices | |
| 650 | 4 | |a Transient analysis | |
| 650 | 4 | |a Augmented complex LMS (ACLMS) | |
| 650 | 4 | |a Stability analysis | |
| 650 | 4 | |a Convergence | |
| 650 | 4 | |a Steady-state | |
| 650 | 4 | |a Estimation | |
| 650 | 4 | |a Standards | |
| 650 | 4 | |a complementary mean square analysis | |
| 650 | 4 | |a second order noncircularity | |
| 700 | 1 | |a Mandic, Danilo |4 oth | |
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10.1109/LSP.2017.2717945 doi PQ20171228 (DE-627)OLC1998316696 (DE-599)GBVOLC1998316696 (PRQ)i653-c280a32b518a5724063aa3186e5d1efab83fbd4964001cc45211e5ec5670fb7d0 (KEY)02390256u00000000000009900001complementarymeansquareanalysisofaugmentedclmsfors DE-627 ger DE-627 rakwb eng 53.00 bkl Xia, Yili verfasserin aut Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. Covariance matrices Transient analysis Augmented complex LMS (ACLMS) Stability analysis Convergence Steady-state Estimation Standards complementary mean square analysis second order noncircularity Mandic, Danilo oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX PP, 99, Seite 1-1 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/LSP.2017.2717945 Volltext http://ieeexplore.ieee.org/document/7954619 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR PP 99 1-1 |
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10.1109/LSP.2017.2717945 doi PQ20171228 (DE-627)OLC1998316696 (DE-599)GBVOLC1998316696 (PRQ)i653-c280a32b518a5724063aa3186e5d1efab83fbd4964001cc45211e5ec5670fb7d0 (KEY)02390256u00000000000009900001complementarymeansquareanalysisofaugmentedclmsfors DE-627 ger DE-627 rakwb eng 53.00 bkl Xia, Yili verfasserin aut Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. Covariance matrices Transient analysis Augmented complex LMS (ACLMS) Stability analysis Convergence Steady-state Estimation Standards complementary mean square analysis second order noncircularity Mandic, Danilo oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX PP, 99, Seite 1-1 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/LSP.2017.2717945 Volltext http://ieeexplore.ieee.org/document/7954619 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR PP 99 1-1 |
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10.1109/LSP.2017.2717945 doi PQ20171228 (DE-627)OLC1998316696 (DE-599)GBVOLC1998316696 (PRQ)i653-c280a32b518a5724063aa3186e5d1efab83fbd4964001cc45211e5ec5670fb7d0 (KEY)02390256u00000000000009900001complementarymeansquareanalysisofaugmentedclmsfors DE-627 ger DE-627 rakwb eng 53.00 bkl Xia, Yili verfasserin aut Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. Covariance matrices Transient analysis Augmented complex LMS (ACLMS) Stability analysis Convergence Steady-state Estimation Standards complementary mean square analysis second order noncircularity Mandic, Danilo oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX PP, 99, Seite 1-1 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/LSP.2017.2717945 Volltext http://ieeexplore.ieee.org/document/7954619 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR PP 99 1-1 |
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10.1109/LSP.2017.2717945 doi PQ20171228 (DE-627)OLC1998316696 (DE-599)GBVOLC1998316696 (PRQ)i653-c280a32b518a5724063aa3186e5d1efab83fbd4964001cc45211e5ec5670fb7d0 (KEY)02390256u00000000000009900001complementarymeansquareanalysisofaugmentedclmsfors DE-627 ger DE-627 rakwb eng 53.00 bkl Xia, Yili verfasserin aut Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. Covariance matrices Transient analysis Augmented complex LMS (ACLMS) Stability analysis Convergence Steady-state Estimation Standards complementary mean square analysis second order noncircularity Mandic, Danilo oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX PP, 99, Seite 1-1 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/LSP.2017.2717945 Volltext http://ieeexplore.ieee.org/document/7954619 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR PP 99 1-1 |
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10.1109/LSP.2017.2717945 doi PQ20171228 (DE-627)OLC1998316696 (DE-599)GBVOLC1998316696 (PRQ)i653-c280a32b518a5724063aa3186e5d1efab83fbd4964001cc45211e5ec5670fb7d0 (KEY)02390256u00000000000009900001complementarymeansquareanalysisofaugmentedclmsfors DE-627 ger DE-627 rakwb eng 53.00 bkl Xia, Yili verfasserin aut Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. Covariance matrices Transient analysis Augmented complex LMS (ACLMS) Stability analysis Convergence Steady-state Estimation Standards complementary mean square analysis second order noncircularity Mandic, Danilo oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX PP, 99, Seite 1-1 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:PP number:99 pages:1-1 http://dx.doi.org/10.1109/LSP.2017.2717945 Volltext http://ieeexplore.ieee.org/document/7954619 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR PP 99 1-1 |
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Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals |
| abstract |
A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. |
| abstractGer |
A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. |
| abstract_unstemmed |
A novel physical insight is provided into the behaviour and performance of the augmented complex least mean square (ACLMS) algorithm for widely linear adaptive estimation of general second order noncircular (improper) Gaussian signals, for which the off-diagonal elements of their covariance and complementary covariance matrices are non-zero. This is achieved through a novel complementary mean square analysis, which serves as a counterpart to the standard mean square analysis, and focuses on the behaviour of the complementary second order statistics of the output error and the augmented weight error vector. Next, for these two key parameters which govern the ACLMS, we establish the effect of the degree of input noncircularity on their evolution. Both transient and steady-state performances are addressed and a stability bound on the step-size for their convergence is established. Simulations in the system identification setting support the analysis. |
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| title_short |
Complementary Mean Square Analysis of Augmented CLMS for Second Order Noncircular Gaussian Signals |
| url |
http://dx.doi.org/10.1109/LSP.2017.2717945 http://ieeexplore.ieee.org/document/7954619 |
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| author2 |
Mandic, Danilo |
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Mandic, Danilo |
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| doi_str |
10.1109/LSP.2017.2717945 |
| up_date |
2024-07-04T04:46:05.673Z |
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