Frequency estimation of undamped exponential signals using genetic algorithms

In this paper, we consider the problem of frequency estimation of undamped superimposed exponential signals model. We propose two iterative techniques of frequency estimation using genetic algorithms. The proposed methods use an elitism based generational genetic algorithm for obtaining the least squares and the approximate least squares estimates. In the simulation studies, it is observed that the proposed methods give nearly efficient estimates, having mean square error almost attaining the corresponding Cramér-Rao lower bound. The proposed methods significantly do not depend on the initial guess values otherwise required for other iterative methods of frequency estimation. It is also observed that the proposed methods have fairly high breakdown point with respect to different types of outliers present in the data. Outlier robustness and accuracy of the proposed methods are compared with the classical approaches for this problem.