IJTEEE
International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
PREVIOUS PUBLICATIONS



IJTEEE >> Volume 2 - Issue 4, April 2014 Edition



International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Performance Analysis Of RLS Over LMS Algorithm For MSE In Adaptive Filters

[Full Text]

 

AUTHOR(S)

Jay Prakash Vijay, Nitin Kumar Sharma

 

KEYWORDS

Index Terms: Adaptive filters, MSE (Mean Square Error), LMS (Least Mean Square), NLMS (Normalized Least Mean Square), RLS (Recursive Least Square), QR-RLS (Quadrative Recursive RLS algorithm)

 

ABSTRACT

Abstract: This paper presents a comparable study of different adaptive filter algorithm LMS, NLMS, RLS and QR-RLS applied in minimization of MSE. In this paper we considered two kinds of scenarios for analyzing their performance. The RLS algorithm has faster convergence speed/rate than LMS algorithms with better robustness to changeable environment and better tracking capability. As well as the MSE curve shows that QR-RLS algorithm outperforms other remaining algorithm like LMS, NLMS and RLS algorithm.

 

REFERENCES

[1] Udawat, A. , Sharma, P.C. ; Katiyal, S., “Performance analysis and comparison of adaptive beam forming algorithms for Smart Antenna Systems”, Next Generation Networks, 2010 International Conference, p.p. 1 – 5, Sept. 2010

[2] Djigan, V.I. , “Adaptive filtering algorithms with quatratized cost function for Linearly Constrained arrays”, Antenna Theory and Techniques (ICATT), 2013 IX International Conference, p.p. 214 – 216, Sep. 2013

[3] Soumya, R.G., Naveen, N., Lal, M.J., “Application of Adaptive Filter Using Adaptive Line Enhancer Techniques” Advances in Computing and Communications (ICACC), 2013 Third International Conference, p.p. 165 – 168, Aug. 2013

[4] Yu Xia “Performance analysis of adaptive filters for time-varying systems”, Control Conference (CCC), 2013 32nd Chinese, p.p. 8572 – 8575, July 2013

[5] Huang, Y.J. Wang, Y.W. ; Meng, F.J. ; Wang, G.L., “A spatial spectrum estimation algorithm based on adaptive beam forming nulling” Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference, p.p. 220 – 224, June 2013

[6] Salman, M.S. , Yilmaz, M.F., “Adaptive filtering for incident plane wave estimation” Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference, p.p. 162 – 165, May 2013.

[7] J. Mehena, L. Swain, G. Patnaik, “System Identification based on QR- Decomposition” Int. J. of Intelligent Computing and Applied Sciences, p.p.31-39, ISSN: 2322-0031, Vol. 1, Issue1 2013.

[8] Huang Quanzhen, Gao Zhiyuan, Gao Shouwei, Shao Yong, Zhu Xiaojin, “Comparison of LMS and RLS Algorithm for active vibration control of smart structures” 3rd International Conference on Measuring Technology and Mechatronics Automation, IEEE , pp. 745-748, 2011

[9] Lei Wang, Rodrigo C. de Lamare, “Constrained Constant Modulus RLS-based Blind Adaptive Beamforming Algorithm for Smart Antennas”, Wireless Communication Systems (ISWCS 2007) 4th International Symposium, p.p. 657 – 661, Oct. 2007

[10] S. Haykin, Adaptive Filter Theory. Prentice Hall, Inc., 1996.

[11] S. Haykin, Modern Filters. Maxwell Macmillan International Editions, 1990.