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
Event	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: 16 Oct 2008 → 19 Oct 2008

Publication series

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

Conference

Conference	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Country/Territory	Mexico
City	Cancun
Period	16/10/08 → 19/10/08

ASJC Scopus subject areas

Artificial Intelligence
Software
Electrical and Electronic Engineering

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10.1109/MLSP.2008.4685517

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Jelfs, B., Xia, Y., Mandic, D. P., & Douglas, S. C. (2008). Collaborative adaptive filtering in the complex domain. In Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 (pp. 421-425). [4685517] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685517

@inproceedings{75a011b1a0c94e3d8cf0e3ee2cf2d0a9,

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",

language = "English",

isbn = "9781424423767",

series = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

pages = "421--425",

booktitle = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

note = "2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 ; Conference date: 16-10-2008 Through 19-10-2008",

}

Jelfs, B, Xia, Y, Mandic, DP & Douglas, SC 2008, Collaborative adaptive filtering in the complex domain. in Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008., 4685517, Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, pp. 421-425, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexico, 16/10/08. https://doi.org/10.1109/MLSP.2008.4685517

Collaborative adaptive filtering in the complex domain. / Jelfs, Beth; Xia, Yili; Mandic, Danilo P. et al.

Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 421-425 4685517 (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008).

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

TY - GEN

T1 - Collaborative adaptive filtering in the complex domain

AU - Jelfs, Beth

AU - Xia, Yili

AU - Mandic, Danilo P.

AU - Douglas, Scott C.

PY - 2008

Y1 - 2008

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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DO - 10.1109/MLSP.2008.4685517

M3 - Conference contribution

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SN - 9781424423767

T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

SP - 421

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

T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Y2 - 16 October 2008 through 19 October 2008

ER -

Collaborative adaptive filtering in the complex domain

Abstract

Publication series

Conference

ASJC Scopus subject areas

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