NDLI: Effective adaptive noise canceller design using normalized LMS

Content Provider	IEEE Xplore Digital Library
Author	Kumari, R. Mehra, R. Shrama, L.
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. & Commun. Eng., NITTTR Chandigarh, Chandigarh, India (Kumari, R.; Mehra, R.; Shrama, L.)
Abstract	This paper presents the application of Adaptive filters in noise cancellation during various communication processes, where non-stationary signals are transmitted. Adaptive filter estimates the noise signal and by applying the appropriate weights the estimated noise signal is eliminated from the information. For noise cancellation applications most efficient Adaptive filter Algorithms LMS and its normalized form NLMS are used and their comparative analysis is done in form of their output power, error power and SNR. In both of the algorithms concept of negative feedback is utilized in the cancellation of noise from the signal and thus both of these are also called negative feedback algorithms. Implementation and analysis is done by applying different step sizes on different order of filter. Order of filter is taken as 4, 8, 12 and then by changing the values of coefficients the graphical and computational analysis is done. Finally an efficient design using NLMS algorithm is implemented where order is taken as 8 and step size 0.2. This results as a low error power (11.7221 db) and a high value of SNR (1.1445) than that of LMS algorithms.
Starting Page	571
Ending Page	575
File Size	700748
Page Count	5
File Format	PDF
e-ISBN	9781467368094
DOI	10.1109/NGCT.2015.7375185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Adaptive Filters Least squares approximations Finite impulse response filters Adaptive filters SNR Filtering algorithms Noise cancellation LMS Algorithm Error Power NLMS Algorithm Signal to noise ratio
Content Type	Text
Resource Type	Article

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