Raj Kumar Thenua et. al. / International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4373-4378 SIMULATION AND PERFORMANCE ANALYASIS OF ADAPTIVE FILTER IN NOISE CANCELLATION RAJ KUMAR THENUA Department of Electronics & Instrumentation, Anand Engineering College, Agra, Uttar Pradesh-282007,India S.K. AGARWAL Department of Electronics & Communication, Sobhasaria Engineering College, Sikar, Rajasthan-332021, India Abstract Noise problems in the environment have gained attention due to the tremendous growth of technology that has led to noisy engines, heavy machinery, high speed wind buffeting and other noise sources. The problem of controlling the noise level has become the focus of a tremendous amount of research over the years. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy tone signal and white noise signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), percentage noise removal, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement. Keywords: Adaptive filter; convergence speed; LMS; Mean Squared Error; NLMS; RLS. 1. Introduction In the process of transmission of information from the source to receiver side, noise from the surroundings automatically gets added to the signal. This acoustic noise [1] picked up by microphone is undesirable, as it reduces the perceived quality or intelligibility of the audio signal. The problem of effective removal or reduction of noise is an active area of research [2]. The usage of adaptive filters is one of the most popular proposed solutions to reduce the signal corruption caused by predictable and unpredictable noise. An adaptive filter [3] has the property of self-modifying its frequency response to change the behavior in time, allowing the filter to adapt the response to the input signal characteristics change. Due to this capability the overall performance and the construction flexibility, the adaptive filters have been employed in many different applications, some of the most important are: telephonic echo cancellation [1], radar signal processing, navigation systems, communications channel equalization and biometrics signals processing. The purpose of an adaptive filter in noise cancellation is to remove the noise from a signal adaptively to improve the signal to noise ratio. Figure 1 shows the diagram of a typical Adaptive Noise Cancellation (ANC) system [4]. The discrete adaptive filter processed the reference signal x(n) to produce the output signal y(n) by a convolution with filter’s weights w(n).The filter output y(n) is subtracted from d(n) to obtain an estimation error e(n). The primary sensor receives noise x 1 (n) which has correlation with noise x(n) in an unknown way. The objective here is to minimize the error signal e(n). This error signal is used to incrementally adjust the filter’s weights for the next time instant. The basic adaptive algorithms which widely used for performing weight updation of an adaptive filter are: the LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and the RLS (Recursive Least Square) algorithm [5]. Among all adaptive algorithms LMS has probably become the most popular for its robustness, good tracking capabilities and simplicity in stationary environment. RLS is best for non-stationary environment with high convergence speed but at the cost of higher complexity. Therefore a tradeoff is required in convergence speed and computational complexity, NLMS provides the right solution. ISSN: 0975-5462 4373