### Abstract

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 language | English (US) |
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Pages (from-to) | 1273-1284 |

Number of pages | 12 |

Journal | Signal Processing |

Volume | 84 |

Issue number | 8 |

DOIs | |

State | Published - Aug 1 2004 |

Externally published | Yes |

### Keywords

- 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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## Cite this

Radenkovic, M., & Bose, T. (2004). Global convergence of a blind multichannel identification algorithm.

*Signal Processing*,*84*(8), 1273-1284. https://doi.org/10.1016/j.sigpro.2004.03.014