THE PERFORMANCE OF THE HYBRID LMS ADAPTIVE ALGORITHM

被引:3
作者
CHERN, SJ
HORNG, JC
WONG, KM
机构
[1] Institute of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung
关键词
NORMALIZED ALGORITHM; HYBRID ALGORITHM; ADAPTIVE LINE-ENHANCER; SWITCHING POINT; CONVERGENCE PROPERTY;
D O I
10.1016/0165-1684(95)00016-7
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The hybrid least mean square (HLMS) adaptive filter is a filter with an adaptation algorithm that is a combination of the conventional LMS algorithm and the normalized LMS (NLMS) algorithm. In this paper, the performance of the HLMS adaptive filtering algorithm is investigated. To do so, an analytical expression, in terms of the transient mean square error (MSE), is derived with application to the adaptive line enhancer (ALE). Based on this expression, we are able to examine the convergence properties of the FILMS. Simulation data using the ALE as an application verifies the accuracy of the analytical results. The performance of the FILMS algorithm is also compared with the conventional LMS algorithm as well as the NLMS algorithm. From the simulation results, we observed that, in general, the FILMS algorithm performs more robustly than the conventional LMS and the NLMS algorithms. Since the FILMS algorithm is a combination of the LMS algorithm and the NLMS algorithm, the selection of the optimum switching point of the FILMS algorithm is also addressed using a numerical approach. Many interesting characteristics of the switching point are obtained which show the relationship with the relevant parameters of the FILMS adaptive filter. The sensitivity of the selection of switching point is also examined.
引用
收藏
页码:67 / 88
页数:22
相关论文
共 20 条
[1]  
Chern, The power effect on the convergence rate of the complex LMS adaptive line enhancer, Proc. National Science Council, R.O.C. Part A: Phys. Engrg., 13, pp. 32-39, (1989)
[2]  
Chern, Chen, The study of hybrid adaptive least mean square (LMS) adaptive filtering algorithm with variable step-size, Proc. National Science Council, R.O.C. Part A: Phys. Engrg., 13, pp. 130-144, (1989)
[3]  
Chern, Chen, High resolution frequency estimation using the sub-optimal eigen-decomposition method and adaptive filtering, Proc. National Science Council, R.O.C. Part A: Phys. Engrg., 14, pp. 304-316, (1990)
[4]  
Cho, Choi, Lee, Adaptive line enhancement by using an IIR notch filter, IEEE Transactions on Acoustics, Speech, and Signal Processing, 37 ASSP, 4, pp. 585-589, (1989)
[5]  
Feuer, Weinstein, Convergence analysis of LMS filters with uncorrelated Gaussian data, IEEE Trans. Acoust. Speech Signal Process, 33 ASSP, 1, pp. 222-228, (1985)
[6]  
Fisher, Bershad, The complex LMS algorithm-Transient weight mean and covariance with applications to the ALE, IEEE Trans. Acoust. Speech Signal Process, 31 ASSP, 1, pp. 34-44, (1983)
[7]  
Griffiths, Rapid measurement of digital instantaneous frequency, IEEE Trans. Acoust. Speech Signal Process, 23 ASSP, 2, pp. 207-222, (1975)
[8]  
Harris, Chabries, Bishop, A variable step-size (VS) adaptive filter algorithm, IEEE Trans. Acoust. Speech Signal Process, 34 ASSP, 2, pp. 309-316, (1986)
[9]  
Haykin, Adaptive Filter Theory, (1986)
[10]  
Kwong, Dual sign algorithm for adaptive filtering, IEEE Transactions on Communications, 34 COM, 12, pp. 1272-1275, (1986)