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 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