Implementation of GSC based subarray adaptive LMS algorithm using Xilinx FPGA

Author(s):  
T. Salim ◽  
M. Trinkle ◽  
R. Drake
2012 ◽  
Vol 621 ◽  
pp. 268-272
Author(s):  
Ye Wu ◽  
Shu Yang ◽  
Xiao Yan Zhao

The basic principle of a new pseudo-Fourier series method is derived in detail in this paper. Simulation experiments show that the algorithm can effectively remove the noise and preserve signal's original energy effectively compared with the filtering results based on adaptive LMS algorithm. In addition, the experiment shows that the filtering speed of like Fourier methods is about 25 times the speed of adaptive LMS algorithm. The four types of ground tides are successfully filtered for picking-up the long-term trend of the tilt observation.


2014 ◽  
Vol 644-650 ◽  
pp. 4103-4106
Author(s):  
Da Ming Wang ◽  
Jian Hui Wang ◽  
Wei Jia Cui ◽  
Xu Hui Yang

LMS algorithm is a kind of classic adaptive algorithms. Although it has the virtue of simple operation, it also shows the defects of relatively slow convergence and big steady state errors in low SNR. To remedy these defects, this paper put forward a new variable steps adaptive LMS algorithm. In the transient state, the learning rate increases slowly with the iteration times which accelerate the convergence rate of LMS algorithm. In the steady state, the learning rate decreases gradually with the iteration times which guarantee the convergence accuracy of LMS algorithm. After this improved algorithm is applied in the design of adaptive wavetrap, the simulation results show that it can not only effectively ease up the conflicts between convergence rates and steady state errors, but also improve the performance of wavetrap in real-time trapping.


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