Parameter estimation and tracking of sinusoid using variable-step-size LMS algorithms

In this paper, we present the performance evaluation of different variable-step-size (VSS) least-mean-square (LMS) algorithms, which are used for parameter estimation of sinusoidal signals in sensor networks. The VSS in LMS algorithm is not only varied based on the error and input signal correlations, but also it can be varied by using sigmoid function as an alternative approach. The performance of VSS-LMS algorithm is appraised on the basis of convergence and tracking characteristics, in terms of mean-squared-error (MSE), when delay as well as amplitude need to be estimated. Simulation results are presented to illustrate efficiency and efficacy of both types of VSS criteria in combination with LMS algorithm.