Collaborative adaptive filtering in the complex domain

Beth Jelfs; Yili Xia; Danilo P. Mandic; Scott C. Douglas

doi:10.1109/MLSP.2008.4685517

Collaborative adaptive filtering in the complex domain

Beth Jelfs^*, Yili Xia, Danilo P. Mandic, Scott C. Douglas

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

A novel hybrid filter combining the complex least mean square (CLMS) and augmented CLMS (ACLMS) algorithms for complex domain adaptive filtering is introduced. The ACLMS has been shown to have improved performance in terms of prediction of non-circular complex data compared to that of the CLMS. By taking advantage of this along with the faster convergence of the CLMS, the hybrid filter is shown to give improved performance over both algorithms for both circular and non-circular data. Simulations on complex-valued synthetic and real world data support the effectiveness of this approach.

Original language	English
Title of host publication	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages	421-425
Number of pages	5
DOIs	https://doi.org/10.1109/MLSP.2008.4685517
Publication status	Published - 2008
Externally published	Yes

ASJC Scopus subject areas

Artificial Intelligence
Software
Electrical and Electronic Engineering

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10.1109/MLSP.2008.4685517

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title = "Collaborative adaptive filtering in the complex domain",

abstract = "A novel hybrid filter combining the complex least mean square (CLMS) and augmented CLMS (ACLMS) algorithms for complex domain adaptive filtering is introduced. The ACLMS has been shown to have improved performance in terms of prediction of non-circular complex data compared to that of the CLMS. By taking advantage of this along with the faster convergence of the CLMS, the hybrid filter is shown to give improved performance over both algorithms for both circular and non-circular data. Simulations on complex-valued synthetic and real world data support the effectiveness of this approach.",

author = "Beth Jelfs and Yili Xia and Mandic, {Danilo P.} and Douglas, {Scott C.}",

year = "2008",

doi = "10.1109/MLSP.2008.4685517",

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AU - Jelfs, Beth

AU - Xia, Yili

AU - Mandic, Danilo P.

AU - Douglas, Scott C.

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N2 - A novel hybrid filter combining the complex least mean square (CLMS) and augmented CLMS (ACLMS) algorithms for complex domain adaptive filtering is introduced. The ACLMS has been shown to have improved performance in terms of prediction of non-circular complex data compared to that of the CLMS. By taking advantage of this along with the faster convergence of the CLMS, the hybrid filter is shown to give improved performance over both algorithms for both circular and non-circular data. Simulations on complex-valued synthetic and real world data support the effectiveness of this approach.

AB - A novel hybrid filter combining the complex least mean square (CLMS) and augmented CLMS (ACLMS) algorithms for complex domain adaptive filtering is introduced. The ACLMS has been shown to have improved performance in terms of prediction of non-circular complex data compared to that of the CLMS. By taking advantage of this along with the faster convergence of the CLMS, the hybrid filter is shown to give improved performance over both algorithms for both circular and non-circular data. Simulations on complex-valued synthetic and real world data support the effectiveness of this approach.

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U2 - 10.1109/MLSP.2008.4685517

DO - 10.1109/MLSP.2008.4685517

M3 - Conference contribution

AN - SCOPUS:58049169305

SN - 9781424423767

SP - 421

EP - 425

BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

ER -

Collaborative adaptive filtering in the complex domain

Abstract

ASJC Scopus subject areas

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