Global convergence of a blind multichannel identification algorithm

Miloje Radenkovic, Tamal Bose

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In this paper, we propose an adaptive algorithm for blind identification of single-input multiple-output (SIMO) systems. The algorithm consists of p-1 parallel recursive estimators, where p is the number of system outputs. We analyze the normalized least-mean square (NLMS) estimator, and the weighted recursive least-squares (WRLS) algorithm. It is proved that parameter estimates converge toward a scalar multiple of the true parameters with probability one. The value of the scaling factor is calculated. Numerically simple p-1 parallel NLMS recursions are potential candidate for real-time blind identification applications.

Original languageEnglish (US)
Pages (from-to)1273-1284
Number of pages12
JournalSignal Processing
Issue number8
StatePublished - Aug 2004


  • Blind identification
  • Global convergence
  • Least mean square estimator
  • Martingale theory
  • Multichannel identification
  • Recursive least square

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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