A broadband Kalman filtering approach to blind multichannel identification

Yuanlei Qi, Feiran Yang*, Ming Wu, Jun Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorre-lation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.

Original languageEnglish
Pages (from-to)788-795
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE102A
Issue number6
DOIs
Publication statusPublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright c 2019 The Institute of Electronics.

ASJC Scopus Subject Areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Adaptive filter
  • Affine projection
  • Blind multichannel identification
  • Kalman filter

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