Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation

Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the pr...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Wenyuan [verfasserIn] Zhao, Haiquan Liu, Qianqian

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Diffusion sign subband adaptive filtering algorithm Individual weighting factor Proportionate adaptive filtering algorithm Impulsive interference

Anmerkung:	© Springer Science+Business Media New York 2016

Übergeordnetes Werk:	Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 36(2016), 6 vom: 28. Sept., Seite 2605-2621
Übergeordnetes Werk:	volume:36 ; year:2016 ; number:6 ; day:28 ; month:09 ; pages:2605-2621

Links:	Volltext

DOI / URN:	10.1007/s00034-016-0426-0

Katalog-ID:	SPR000564044

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520			\|a Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts.
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650		4	\|a Individual weighting factor \|7 (dpeaa)DE-He213
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700	1		\|a Zhao, Haiquan \|4 aut
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912			\|a GBV_ILN_602
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publishDate	2016
allfields	10.1007/s00034-016-0426-0 doi (DE-627)SPR000564044 (SPR)s00034-016-0426-0-e DE-627 ger DE-627 rakwb eng Wang, Wenyuan verfasserin aut Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. Diffusion sign subband adaptive filtering algorithm (dpeaa)DE-He213 Individual weighting factor (dpeaa)DE-He213 Proportionate adaptive filtering algorithm (dpeaa)DE-He213 Impulsive interference (dpeaa)DE-He213 Zhao, Haiquan aut Liu, Qianqian aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 6 vom: 28. Sept., Seite 2605-2621 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621 https://dx.doi.org/10.1007/s00034-016-0426-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 6 28 09 2605-2621
spelling	10.1007/s00034-016-0426-0 doi (DE-627)SPR000564044 (SPR)s00034-016-0426-0-e DE-627 ger DE-627 rakwb eng Wang, Wenyuan verfasserin aut Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. Diffusion sign subband adaptive filtering algorithm (dpeaa)DE-He213 Individual weighting factor (dpeaa)DE-He213 Proportionate adaptive filtering algorithm (dpeaa)DE-He213 Impulsive interference (dpeaa)DE-He213 Zhao, Haiquan aut Liu, Qianqian aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 6 vom: 28. Sept., Seite 2605-2621 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621 https://dx.doi.org/10.1007/s00034-016-0426-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 6 28 09 2605-2621
allfields_unstemmed	10.1007/s00034-016-0426-0 doi (DE-627)SPR000564044 (SPR)s00034-016-0426-0-e DE-627 ger DE-627 rakwb eng Wang, Wenyuan verfasserin aut Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. Diffusion sign subband adaptive filtering algorithm (dpeaa)DE-He213 Individual weighting factor (dpeaa)DE-He213 Proportionate adaptive filtering algorithm (dpeaa)DE-He213 Impulsive interference (dpeaa)DE-He213 Zhao, Haiquan aut Liu, Qianqian aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 6 vom: 28. Sept., Seite 2605-2621 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621 https://dx.doi.org/10.1007/s00034-016-0426-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 6 28 09 2605-2621
allfieldsGer	10.1007/s00034-016-0426-0 doi (DE-627)SPR000564044 (SPR)s00034-016-0426-0-e DE-627 ger DE-627 rakwb eng Wang, Wenyuan verfasserin aut Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. Diffusion sign subband adaptive filtering algorithm (dpeaa)DE-He213 Individual weighting factor (dpeaa)DE-He213 Proportionate adaptive filtering algorithm (dpeaa)DE-He213 Impulsive interference (dpeaa)DE-He213 Zhao, Haiquan aut Liu, Qianqian aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 6 vom: 28. Sept., Seite 2605-2621 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621 https://dx.doi.org/10.1007/s00034-016-0426-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 6 28 09 2605-2621
allfieldsSound	10.1007/s00034-016-0426-0 doi (DE-627)SPR000564044 (SPR)s00034-016-0426-0-e DE-627 ger DE-627 rakwb eng Wang, Wenyuan verfasserin aut Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. Diffusion sign subband adaptive filtering algorithm (dpeaa)DE-He213 Individual weighting factor (dpeaa)DE-He213 Proportionate adaptive filtering algorithm (dpeaa)DE-He213 Impulsive interference (dpeaa)DE-He213 Zhao, Haiquan aut Liu, Qianqian aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 36(2016), 6 vom: 28. Sept., Seite 2605-2621 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621 https://dx.doi.org/10.1007/s00034-016-0426-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2016 6 28 09 2605-2621
language	English
source	Enthalten in Circuits, systems and signal processing 36(2016), 6 vom: 28. Sept., Seite 2605-2621 volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621
sourceStr	Enthalten in Circuits, systems and signal processing 36(2016), 6 vom: 28. Sept., Seite 2605-2621 volume:36 year:2016 number:6 day:28 month:09 pages:2605-2621
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Diffusion sign subband adaptive filtering algorithm Individual weighting factor Proportionate adaptive filtering algorithm Impulsive interference
isfreeaccess_bool	false
container_title	Circuits, systems and signal processing
authorswithroles_txt_mv	Wang, Wenyuan @@aut@@ Zhao, Haiquan @@aut@@ Liu, Qianqian @@aut@@
publishDateDaySort_date	2016-09-28T00:00:00Z
hierarchy_top_id	351975470
id	SPR000564044
language_de	englisch
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author	Wang, Wenyuan
spellingShingle	Wang, Wenyuan misc Diffusion sign subband adaptive filtering algorithm misc Individual weighting factor misc Proportionate adaptive filtering algorithm misc Impulsive interference Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation
authorStr	Wang, Wenyuan
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topic_title	Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation Diffusion sign subband adaptive filtering algorithm (dpeaa)DE-He213 Individual weighting factor (dpeaa)DE-He213 Proportionate adaptive filtering algorithm (dpeaa)DE-He213 Impulsive interference (dpeaa)DE-He213
topic	misc Diffusion sign subband adaptive filtering algorithm misc Individual weighting factor misc Proportionate adaptive filtering algorithm misc Impulsive interference
topic_unstemmed	misc Diffusion sign subband adaptive filtering algorithm misc Individual weighting factor misc Proportionate adaptive filtering algorithm misc Impulsive interference
topic_browse	misc Diffusion sign subband adaptive filtering algorithm misc Individual weighting factor misc Proportionate adaptive filtering algorithm misc Impulsive interference
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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hierarchy_parent_title	Circuits, systems and signal processing
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title	Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation
ctrlnum	(DE-627)SPR000564044 (SPR)s00034-016-0426-0-e
title_full	Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation
author_sort	Wang, Wenyuan
journal	Circuits, systems and signal processing
journalStr	Circuits, systems and signal processing
lang_code	eng
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publishDateSort	2016
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container_start_page	2605
author_browse	Wang, Wenyuan Zhao, Haiquan Liu, Qianqian
container_volume	36
format_se	Elektronische Aufsätze
author-letter	Wang, Wenyuan
doi_str_mv	10.1007/s00034-016-0426-0
title_sort	diffusion sign subband adaptive filtering algorithm with individual weighting factors for distributed estimation
title_auth	Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation
abstract	Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. © Springer Science+Business Media New York 2016
abstractGer	Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. © Springer Science+Business Media New York 2016
abstract_unstemmed	Abstract A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price’s theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. © Springer Science+Business Media New York 2016
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title_short	Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation
url	https://dx.doi.org/10.1007/s00034-016-0426-0
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author2	Zhao, Haiquan Liu, Qianqian
author2Str	Zhao, Haiquan Liu, Qianqian
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doi_str	10.1007/s00034-016-0426-0
up_date	2024-07-03T16:56:34.658Z
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