RLS Adaptive Filter With Inequality Constraints
In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nascimento, Vitor H [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE signal processing letters - Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5, New York, NY, 19XX, 23(2016), 5, Seite 752-756 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2016 ; number:5 ; pages:752-756 |
| Links: |
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| DOI / URN: |
10.1109/LSP.2016.2551468 |
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OLC1975987675 |
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| 520 | |a In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. | ||
| 650 | 4 | |a Indexes | |
| 650 | 4 | |a Upper bound | |
| 650 | 4 | |a inequality constraint | |
| 650 | 4 | |a Signal processing algorithms | |
| 650 | 4 | |a adaptive filter | |
| 650 | 4 | |a Approximation algorithms | |
| 650 | 4 | |a Hardware | |
| 650 | 4 | |a non-negativity | |
| 650 | 4 | |a Estimation | |
| 650 | 4 | |a box constraint | |
| 650 | 4 | |a RLS-DCD | |
| 650 | 4 | |a Complexity theory | |
| 700 | 1 | |a Zakharov, Yuriy V |4 oth | |
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10.1109/LSP.2016.2551468 doi PQ20160610 (DE-627)OLC1975987675 (DE-599)GBVOLC1975987675 (PRQ)i534-561e90561f9794189fff7586744ffce20e10e6331e458d4c7945f972d0a169d0 (KEY)02390256u20160000023000500752rlsadaptivefilterwithinequalityconstraints DE-627 ger DE-627 rakwb eng 53.00 bkl Nascimento, Vitor H verfasserin aut RLS Adaptive Filter With Inequality Constraints 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. Indexes Upper bound inequality constraint Signal processing algorithms adaptive filter Approximation algorithms Hardware non-negativity Estimation box constraint RLS-DCD Complexity theory Zakharov, Yuriy V oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 5, Seite 752-756 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:5 pages:752-756 http://dx.doi.org/10.1109/LSP.2016.2551468 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7448422 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 5 752-756 |
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10.1109/LSP.2016.2551468 doi PQ20160610 (DE-627)OLC1975987675 (DE-599)GBVOLC1975987675 (PRQ)i534-561e90561f9794189fff7586744ffce20e10e6331e458d4c7945f972d0a169d0 (KEY)02390256u20160000023000500752rlsadaptivefilterwithinequalityconstraints DE-627 ger DE-627 rakwb eng 53.00 bkl Nascimento, Vitor H verfasserin aut RLS Adaptive Filter With Inequality Constraints 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. Indexes Upper bound inequality constraint Signal processing algorithms adaptive filter Approximation algorithms Hardware non-negativity Estimation box constraint RLS-DCD Complexity theory Zakharov, Yuriy V oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 5, Seite 752-756 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:5 pages:752-756 http://dx.doi.org/10.1109/LSP.2016.2551468 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7448422 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 5 752-756 |
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10.1109/LSP.2016.2551468 doi PQ20160610 (DE-627)OLC1975987675 (DE-599)GBVOLC1975987675 (PRQ)i534-561e90561f9794189fff7586744ffce20e10e6331e458d4c7945f972d0a169d0 (KEY)02390256u20160000023000500752rlsadaptivefilterwithinequalityconstraints DE-627 ger DE-627 rakwb eng 53.00 bkl Nascimento, Vitor H verfasserin aut RLS Adaptive Filter With Inequality Constraints 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. Indexes Upper bound inequality constraint Signal processing algorithms adaptive filter Approximation algorithms Hardware non-negativity Estimation box constraint RLS-DCD Complexity theory Zakharov, Yuriy V oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 5, Seite 752-756 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:5 pages:752-756 http://dx.doi.org/10.1109/LSP.2016.2551468 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7448422 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 5 752-756 |
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10.1109/LSP.2016.2551468 doi PQ20160610 (DE-627)OLC1975987675 (DE-599)GBVOLC1975987675 (PRQ)i534-561e90561f9794189fff7586744ffce20e10e6331e458d4c7945f972d0a169d0 (KEY)02390256u20160000023000500752rlsadaptivefilterwithinequalityconstraints DE-627 ger DE-627 rakwb eng 53.00 bkl Nascimento, Vitor H verfasserin aut RLS Adaptive Filter With Inequality Constraints 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. Indexes Upper bound inequality constraint Signal processing algorithms adaptive filter Approximation algorithms Hardware non-negativity Estimation box constraint RLS-DCD Complexity theory Zakharov, Yuriy V oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 5, Seite 752-756 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:5 pages:752-756 http://dx.doi.org/10.1109/LSP.2016.2551468 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7448422 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 5 752-756 |
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10.1109/LSP.2016.2551468 doi PQ20160610 (DE-627)OLC1975987675 (DE-599)GBVOLC1975987675 (PRQ)i534-561e90561f9794189fff7586744ffce20e10e6331e458d4c7945f972d0a169d0 (KEY)02390256u20160000023000500752rlsadaptivefilterwithinequalityconstraints DE-627 ger DE-627 rakwb eng 53.00 bkl Nascimento, Vitor H verfasserin aut RLS Adaptive Filter With Inequality Constraints 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. Indexes Upper bound inequality constraint Signal processing algorithms adaptive filter Approximation algorithms Hardware non-negativity Estimation box constraint RLS-DCD Complexity theory Zakharov, Yuriy V oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 5, Seite 752-756 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:5 pages:752-756 http://dx.doi.org/10.1109/LSP.2016.2551468 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7448422 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 5 752-756 |
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RLS Adaptive Filter With Inequality Constraints |
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In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. |
| abstractGer |
In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. |
| abstract_unstemmed |
In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. |
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| title_short |
RLS Adaptive Filter With Inequality Constraints |
| url |
http://dx.doi.org/10.1109/LSP.2016.2551468 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7448422 |
| remote_bool |
false |
| author2 |
Zakharov, Yuriy V |
| author2Str |
Zakharov, Yuriy V |
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182273075 |
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| author2_role |
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| doi_str |
10.1109/LSP.2016.2551468 |
| up_date |
2024-07-03T14:18:17.496Z |
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7.401597 |