Least-squares optimal variable step-size LMS for nonblind system identification with noise
Least-squares optimal variable step-size LMS for nonblind system identification with noise
复制标题
用于带噪声非盲系统识别的最小二乘最优变步长 LMS
DOI：
10.1109/icece.2008.4769245
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Md. Kamrul Hasan
中科院分区：
文献类型：
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作者：
Muhammad A. Wahab;M. A. Uzzaman;M. S. Hai;M. A. Haque;Md. Kamrul Hasan
针对单输入单输出（SISO）有限冲激响应系统的非盲辨识问题，提出了一种最小二乘最优变步长（LSVSS）最小均方（LMS）自适应算法。结果表明，著名的归一化LMS（NLMS）和LSVSS-LMS算法是数学上等价的无噪声的情况下。推导的LSVSS，然后扩展到有噪声的测量。最后给出了LSVSS-LMS算法的收敛性分析。该方法的性能与传统的鲁棒变步长LMS算法进行了比较。实验结果表明，改进的非盲系统辨识算法在平稳和非平稳环境中的性能。
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-stepsize LMS algorithms. Experimental results demonstrate improved performance of the proposed algorithm for nonblind system identification in both stationary and nonstationary environments.