Least-Squares Optimal Variable Step-Size LMS for Nonblind System Identification with Noise

被引：4
|
作者：
Wahab, M. A. [1 ]
Uzzaman, M. Adel [1 ]
Hai, M. S. [1 ]
Haque, M. A. [1 ]
Hasan, M. K. [1 ]
机构：
[1] Bangladesh Univ Engn & Technol, Dept Elect & Elect Engn, Dhaka 1000, Bangladesh
来源：
PROCEEDINGS OF ICECE 2008, VOLS 1 AND 2 | 2008年
关键词：
D O I：
10.1109/ICECE.2008.4769245
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper proposes a least-square optimal variable step-size (LSVSS) least-mean-square (LMS) adaptive algorithm for nonblind identification of single-input single-output (SISO) finite impulse response systems. It is shown that the well-known normalized LMS (NLMS) and the LSVSS-LMS algorithms are mathematically equivalent for the noise-free case. The derivation of LSVSS is then extended for noisy measurements. The convergence analysis of the LSVSS-LMS is also presented. The performance of the proposed method is compared with conventional robust variable-step-size LMS algorithms. Experimental res. nits demonstrate improved performance of the proposed algorithm for nonblind system identification in both stationary and nonstationary environments.
引用
收藏
页码：428 / 433
页数：6
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