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 proceedingConference contribution

8 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Number of pages5
Publication statusPublished - 2008
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering


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