Implementation of adaptive filtering algorithms for noise cancellation
Abstract: This paper deals with the implementation and performance evaluation of adaptive filtering algorithms for noise cancellation without reference signal. Noise cancellation is a technique of estimating a desired signal from a noise-corrupted observation. If the signal and noise characteristics are unknown or change continuously over time, the need of adaptive filter arises. In contrast to the conventional digital filter design techniques, adaptive filters do not have constant filter parameters, they have the capability to continuously adjust their coefficients to their operating environment. To design an adaptive filter, that produces an optimum estimate of the desired signal from the noisy environment, different adaptive filtering algorithms are implemented and compared to each other. The Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm are investigated. Three performance criteria are used in the study of these algorithms: the rate of convergence, the error performance and the signal-to-noise ratio SNR. The implementation results show that the adaptive noise cancellation application benefits more from the use of the NLMS algorithm instead of the LMS or RLS algorithm.
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