Complex dual channel estimation: Cost effective widely linear adaptive filtering
Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jahanchahi, Cyrus [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery - Zhang, Yaoyao ELSEVIER, 2014transfer abstract, a European journal devoted to the methods and applications of signal processing, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:104 ; year:2014 ; pages:33-42 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.sigpro.2014.03.050 |
|---|
| Katalog-ID: |
ELV027875318 |
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| 520 | |a Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. | ||
| 520 | |a Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. | ||
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10.1016/j.sigpro.2014.03.050 doi GBVA2014004000015.pica (DE-627)ELV027875318 (ELSEVIER)S0165-1684(14)00155-8 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 620 VZ 690 VZ 50.92 bkl Jahanchahi, Cyrus verfasserin aut Complex dual channel estimation: Cost effective widely linear adaptive filtering 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. CLMS Elsevier Complex noncircularity Elsevier ACLMS Elsevier Augmented statistics Elsevier Widely linear estimation Elsevier Kanna, Sithan oth Mandic, Danilo oth Enthalten in Elsevier Zhang, Yaoyao ELSEVIER Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery 2014transfer abstract a European journal devoted to the methods and applications of signal processing Amsterdam [u.a.] (DE-627)ELV017513162 volume:104 year:2014 pages:33-42 extent:10 https://doi.org/10.1016/j.sigpro.2014.03.050 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 50.92 Meerestechnik VZ AR 104 2014 33-42 10 045F 004 |
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10.1016/j.sigpro.2014.03.050 doi GBVA2014004000015.pica (DE-627)ELV027875318 (ELSEVIER)S0165-1684(14)00155-8 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 620 VZ 690 VZ 50.92 bkl Jahanchahi, Cyrus verfasserin aut Complex dual channel estimation: Cost effective widely linear adaptive filtering 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. CLMS Elsevier Complex noncircularity Elsevier ACLMS Elsevier Augmented statistics Elsevier Widely linear estimation Elsevier Kanna, Sithan oth Mandic, Danilo oth Enthalten in Elsevier Zhang, Yaoyao ELSEVIER Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery 2014transfer abstract a European journal devoted to the methods and applications of signal processing Amsterdam [u.a.] (DE-627)ELV017513162 volume:104 year:2014 pages:33-42 extent:10 https://doi.org/10.1016/j.sigpro.2014.03.050 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 50.92 Meerestechnik VZ AR 104 2014 33-42 10 045F 004 |
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10.1016/j.sigpro.2014.03.050 doi GBVA2014004000015.pica (DE-627)ELV027875318 (ELSEVIER)S0165-1684(14)00155-8 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 620 VZ 690 VZ 50.92 bkl Jahanchahi, Cyrus verfasserin aut Complex dual channel estimation: Cost effective widely linear adaptive filtering 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. CLMS Elsevier Complex noncircularity Elsevier ACLMS Elsevier Augmented statistics Elsevier Widely linear estimation Elsevier Kanna, Sithan oth Mandic, Danilo oth Enthalten in Elsevier Zhang, Yaoyao ELSEVIER Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery 2014transfer abstract a European journal devoted to the methods and applications of signal processing Amsterdam [u.a.] (DE-627)ELV017513162 volume:104 year:2014 pages:33-42 extent:10 https://doi.org/10.1016/j.sigpro.2014.03.050 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 50.92 Meerestechnik VZ AR 104 2014 33-42 10 045F 004 |
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10.1016/j.sigpro.2014.03.050 doi GBVA2014004000015.pica (DE-627)ELV027875318 (ELSEVIER)S0165-1684(14)00155-8 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 620 VZ 690 VZ 50.92 bkl Jahanchahi, Cyrus verfasserin aut Complex dual channel estimation: Cost effective widely linear adaptive filtering 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. CLMS Elsevier Complex noncircularity Elsevier ACLMS Elsevier Augmented statistics Elsevier Widely linear estimation Elsevier Kanna, Sithan oth Mandic, Danilo oth Enthalten in Elsevier Zhang, Yaoyao ELSEVIER Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery 2014transfer abstract a European journal devoted to the methods and applications of signal processing Amsterdam [u.a.] (DE-627)ELV017513162 volume:104 year:2014 pages:33-42 extent:10 https://doi.org/10.1016/j.sigpro.2014.03.050 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 50.92 Meerestechnik VZ AR 104 2014 33-42 10 045F 004 |
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10.1016/j.sigpro.2014.03.050 doi GBVA2014004000015.pica (DE-627)ELV027875318 (ELSEVIER)S0165-1684(14)00155-8 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 620 VZ 690 VZ 50.92 bkl Jahanchahi, Cyrus verfasserin aut Complex dual channel estimation: Cost effective widely linear adaptive filtering 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. CLMS Elsevier Complex noncircularity Elsevier ACLMS Elsevier Augmented statistics Elsevier Widely linear estimation Elsevier Kanna, Sithan oth Mandic, Danilo oth Enthalten in Elsevier Zhang, Yaoyao ELSEVIER Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery 2014transfer abstract a European journal devoted to the methods and applications of signal processing Amsterdam [u.a.] (DE-627)ELV017513162 volume:104 year:2014 pages:33-42 extent:10 https://doi.org/10.1016/j.sigpro.2014.03.050 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 50.92 Meerestechnik VZ AR 104 2014 33-42 10 045F 004 |
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Enthalten in Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery Amsterdam [u.a.] volume:104 year:2014 pages:33-42 extent:10 |
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Enthalten in Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery Amsterdam [u.a.] volume:104 year:2014 pages:33-42 extent:10 |
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Influence of Sc3+ doping in B-site on electrochemical performance of Li4Ti5O12 anode materials for lithium-ion battery |
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Jahanchahi, Cyrus @@aut@@ Kanna, Sithan @@oth@@ Mandic, Danilo @@oth@@ |
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Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. |
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Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. |
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Widely linear estimation for complex-valued data allows for a unified treatment of both second order circular (proper) and non-circular (improper) signals. We propose the complex dual channel (CDC) estimation technique as an alternative to widely linear estimation to both gain further insights into complex valued minimum mean square error (MMSE) estimation and to design computationally efficient adaptive filtering algorithms. This is achieved by finding two sets of optimal weights that minimize the mean square error (MSE) in estimating the real and imaginary parts of the signal independently. The concept is used in a stochastic gradient setting to design the dual channel complex least mean square (DC-CLMS). The analysis shows that any one of the sub-filters within the DC-CLMS can be used to estimate strictly linear models while the DC-CLMS is equivalent to widely linear estimation. This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms. |
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