<p>Robust spectral analysis of sinusoidal signals in presence of impulsive noise environment is an important problem in the Statistical Signals Processing. In this paper we address the issue of robust estimation of the parameters of multicomponent sinusoidal model in presence of heavy tail impulsive noise. It is well known that although the least squares estimators are the most efficient estimators, in presence of outliers they perform quite poorly. On the other hand some of the standard robust estimators like the least absolute deviation estimators or Huber-M estimators are very difficult to implement in practice. Moreover, establishing the properties of the least absolute deviation estimators or Huber-M estimators in presence of heavy tail impulsive error is quite challenging. In this paper we have proposed to use sequential weighted least squares estimators, which are easy to implement in practice even when the number of components is quite large. Theoretical properties of the proposed estimators namely the consistency and the asymptotic normality have been established. Extensive simulations indicate that the performances of the proposed estimators are quite comparable with the other robust estimators like the least absolute deviation estimators and Huber-M estimators. It is observed that the performances of the proposed estimators depend on the choice of the weight function, and we have proposed two methods how the proper weight function can be chosen in practice. An illustrative example has been provided to show how the proposed method can be used in practice.</p>