Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering

Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum co...
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Autor*in:	Qiu, Chen [verfasserIn] Ruan, Zongli Qian, Guobing

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Maximum complex correntropy criterion Fixed point Widely linear Bias compensated Estimated input

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 42(2022), 5 vom: 07. Dez., Seite 2959-2985
Übergeordnetes Werk:	volume:42 ; year:2022 ; number:5 ; day:07 ; month:12 ; pages:2959-2985

Links:	Volltext

DOI / URN:	10.1007/s00034-022-02247-5

Katalog-ID:	SPR050047604

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520			\|a Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance.
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allfields	10.1007/s00034-022-02247-5 doi (DE-627)SPR050047604 (SPR)s00034-022-02247-5-e DE-627 ger DE-627 rakwb eng Qiu, Chen verfasserin aut Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. Maximum complex correntropy criterion (dpeaa)DE-He213 Fixed point (dpeaa)DE-He213 Widely linear (dpeaa)DE-He213 Bias compensated (dpeaa)DE-He213 Estimated input (dpeaa)DE-He213 Ruan, Zongli aut Qian, Guobing (orcid)0000-0003-0470-0154 aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 5 vom: 07. Dez., Seite 2959-2985 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985 https://dx.doi.org/10.1007/s00034-022-02247-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 5 07 12 2959-2985
spelling	10.1007/s00034-022-02247-5 doi (DE-627)SPR050047604 (SPR)s00034-022-02247-5-e DE-627 ger DE-627 rakwb eng Qiu, Chen verfasserin aut Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. Maximum complex correntropy criterion (dpeaa)DE-He213 Fixed point (dpeaa)DE-He213 Widely linear (dpeaa)DE-He213 Bias compensated (dpeaa)DE-He213 Estimated input (dpeaa)DE-He213 Ruan, Zongli aut Qian, Guobing (orcid)0000-0003-0470-0154 aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 5 vom: 07. Dez., Seite 2959-2985 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985 https://dx.doi.org/10.1007/s00034-022-02247-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 5 07 12 2959-2985
allfields_unstemmed	10.1007/s00034-022-02247-5 doi (DE-627)SPR050047604 (SPR)s00034-022-02247-5-e DE-627 ger DE-627 rakwb eng Qiu, Chen verfasserin aut Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. Maximum complex correntropy criterion (dpeaa)DE-He213 Fixed point (dpeaa)DE-He213 Widely linear (dpeaa)DE-He213 Bias compensated (dpeaa)DE-He213 Estimated input (dpeaa)DE-He213 Ruan, Zongli aut Qian, Guobing (orcid)0000-0003-0470-0154 aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 5 vom: 07. Dez., Seite 2959-2985 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985 https://dx.doi.org/10.1007/s00034-022-02247-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 5 07 12 2959-2985
allfieldsGer	10.1007/s00034-022-02247-5 doi (DE-627)SPR050047604 (SPR)s00034-022-02247-5-e DE-627 ger DE-627 rakwb eng Qiu, Chen verfasserin aut Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. Maximum complex correntropy criterion (dpeaa)DE-He213 Fixed point (dpeaa)DE-He213 Widely linear (dpeaa)DE-He213 Bias compensated (dpeaa)DE-He213 Estimated input (dpeaa)DE-He213 Ruan, Zongli aut Qian, Guobing (orcid)0000-0003-0470-0154 aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 5 vom: 07. Dez., Seite 2959-2985 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985 https://dx.doi.org/10.1007/s00034-022-02247-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 5 07 12 2959-2985
allfieldsSound	10.1007/s00034-022-02247-5 doi (DE-627)SPR050047604 (SPR)s00034-022-02247-5-e DE-627 ger DE-627 rakwb eng Qiu, Chen verfasserin aut Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. Maximum complex correntropy criterion (dpeaa)DE-He213 Fixed point (dpeaa)DE-He213 Widely linear (dpeaa)DE-He213 Bias compensated (dpeaa)DE-He213 Estimated input (dpeaa)DE-He213 Ruan, Zongli aut Qian, Guobing (orcid)0000-0003-0470-0154 aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 5 vom: 07. Dez., Seite 2959-2985 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985 https://dx.doi.org/10.1007/s00034-022-02247-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 5 07 12 2959-2985
language	English
source	Enthalten in Circuits, systems and signal processing 42(2022), 5 vom: 07. Dez., Seite 2959-2985 volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985
sourceStr	Enthalten in Circuits, systems and signal processing 42(2022), 5 vom: 07. Dez., Seite 2959-2985 volume:42 year:2022 number:5 day:07 month:12 pages:2959-2985
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Maximum complex correntropy criterion Fixed point Widely linear Bias compensated Estimated input
isfreeaccess_bool	false
container_title	Circuits, systems and signal processing
authorswithroles_txt_mv	Qiu, Chen @@aut@@ Ruan, Zongli @@aut@@ Qian, Guobing @@aut@@
publishDateDaySort_date	2022-12-07T00:00:00Z
hierarchy_top_id	351975470
id	SPR050047604
language_de	englisch
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author	Qiu, Chen
spellingShingle	Qiu, Chen misc Maximum complex correntropy criterion misc Fixed point misc Widely linear misc Bias compensated misc Estimated input Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering
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topic_title	Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering Maximum complex correntropy criterion (dpeaa)DE-He213 Fixed point (dpeaa)DE-He213 Widely linear (dpeaa)DE-He213 Bias compensated (dpeaa)DE-He213 Estimated input (dpeaa)DE-He213
topic	misc Maximum complex correntropy criterion misc Fixed point misc Widely linear misc Bias compensated misc Estimated input
topic_unstemmed	misc Maximum complex correntropy criterion misc Fixed point misc Widely linear misc Bias compensated misc Estimated input
topic_browse	misc Maximum complex correntropy criterion misc Fixed point misc Widely linear misc Bias compensated misc Estimated input
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering
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title_full	Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering
author_sort	Qiu, Chen
journal	Circuits, systems and signal processing
journalStr	Circuits, systems and signal processing
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author_browse	Qiu, Chen Ruan, Zongli Qian, Guobing
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format_se	Elektronische Aufsätze
author-letter	Qiu, Chen
doi_str_mv	10.1007/s00034-022-02247-5
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title_sort	fixed-point widely linear mccc for bias-compensated adaptive filtering
title_auth	Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering
abstract	Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering
url	https://dx.doi.org/10.1007/s00034-022-02247-5
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author2	Ruan, Zongli Qian, Guobing
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doi_str	10.1007/s00034-022-02247-5
up_date	2024-07-04T03:14:58.814Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR050047604</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230417082720.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230417s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00034-022-02247-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR050047604</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00034-022-02247-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Qiu, Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fixed-Point Widely Linear MCCC for Bias-Compensated Adaptive Filtering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, the widely linear complex-valued estimated-input adaptive filter (WLC-EIAF) used in processing noisy inputs and outputs has received increasing attention in machine learning and signal processing. In this paper, a fixed-point widely linear complex-valued estimated-input maximum complex correntropy criterion (FPWLC-EIMCCC) algorithm is proposed to deal with noncircular complex signals with noise in input and output. Compared with the existing algorithms, benefiting from the fixed-point method, the proposed FPWLC-EIMCCC can simultaneously have better steady-state performance and faster convergence speed. Furthermore, the theoretical analysis of the FPWLC-EIMCCC is performed by transforming FPWLC-EIMCCC into a gradient-like version based on the matrix inverse lemma and some approximations. Simulation results on system identification, channel estimation, and wind prediction show that FPWLC-EIMCCC significantly improves the filtering performance.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Maximum complex correntropy criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fixed point</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Widely linear</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bias compensated</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimated input</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ruan, Zongli</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Guobing</subfield><subfield code="0">(orcid)0000-0003-0470-0154</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Circuits, systems and signal processing</subfield><subfield code="d">Boston, Mass. : Birkhäuser, 1982</subfield><subfield code="g">42(2022), 5 vom: 07. 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