Spline Adaptive Filtering based on Normalised Least Mean Square Algorithm

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Suchada Sitjongsataporn
Panavy Pookaiyaudom
Panom Petchjatuporn
Thanwa Sripamong


This paper presents the spline adaptive filtering based on normalized least mean square (NLMS) algorithm. The proposed spline adaptive filtering can improve and develop their coefficient vectors, which can converge to optimum values. The basic theory of spline adaptive filtering is based on adaptive NLMS algorithm in comparison with the conventional adaptive least mean square (LMS) algorithm. Adaptive step-size algorithm is derived with an adaptive averaging algorithm. Simulation results show that the proposed NLMS based on spline adaptive filtering with adaptive averaging step-size algorithm can reduce the estimated error rate of proposed NLMS algorithm lower than the conventional LMS algorithm and converge dramatically to the steady-state. 

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