Multikernel adaptive filtering

Research output: Contribution to journalArticlepeer-review

124 Citations (Scopus)


This paper exemplifies that the use of multiple kernels leads to efficient adaptive filtering for nonlinear systems. Two types of multikernel adaptive filtering algorithms are proposed. One is a simple generalization of the kernel normalized least mean square (KNLMS) algorithm , adopting a coherence criterion for dictionary designing. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function plus a weighted block l 1 norm penalty, encouraging the sparsity of an adaptive filter at the block level for efficiency. The proposed multikernel approach enjoys a higher degree of freedom than those approaches which design a kernel as a convex combination of multiple kernels. Numerical examples show that the proposed approach achieves significant gains particularly for nonstationary data as well as insensitivity to the choice of some design-parameters.

Original languageEnglish
Article number6203609
Pages (from-to)4672-4682
Number of pages11
JournalIEEE Transactions on Signal Processing
Issue number9
Publication statusPublished - 2012
Externally publishedYes


  • Block soft-thresholding operator
  • kernel adaptive filtering
  • reproducing kernel Hilbert space

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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