Performance Comparison of LMS, NLMS, RLS Adaptive Filtering Techniques

M. Koteswara Rao*, I. Santhi Prabha**
* Associate Professor, Department of Electronics and Communication Engineering, Srivasavi Engineering College, Tadepalligudem, Andhra Pradesh, India.
** Professor, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh , India.
Periodicity:March - May'2016


This paper gives information about the comparison of adaptive filtering techniques like fixed LMS, Normalized Least Mean Square, and RLS for noise elimination in speech communication systems. The main objective of this paper is to suppress the additive noise which is due to the effect of environmental conditions in the communication systems. In these days, additive noise is one of the major problems in the communication, especially in the digital electronic circuit design. The origins of additive noise are because of atmospheric conditions, weather situations around the system and any other disturbances. Generally, the coefficients of filter updation in a basic filter does not occur time to time, as it may affect the desired information. By updating the coefficients of the filter time to time, this problem could be eradicated and thereby increasing the number of iterations for the filtering process, which gives efficient results. In the communication systems, the performance of these adaptive filters are in terms of mean square error, signal to noise ratio, rate of convergence, etc. In this research paper, the authors have discussed about how to cancel out the additive noise which is combined to the input speech signals that observes the records of signal to noise ratio, and mean square error. Finally, this article compares those results experimentally with the help of MATLAB programming and calculation tool. The mean square error improvement with the number of iterations for different noise signals are represented graphically. By the observation of the graphical results, the rate of convergence and reception level for the given speech and noise signals were found.


Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), Matlab

How to Cite this Article?

Rao, M.K., and Prabha, I.S. (2016). Performance Comparison of LMS, NLMS, RLS Adaptive Filtering Techniques. i-manager's Journal on Electronics Engineering, 6(3), 1-6.


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