Spectral estimation under nature missing data

This paper considers the problem of estimating the autoregressive moving average (ARMA) power spectral density when measurements are corrupted by noises and with missing data. The missing data model is based on an unknown probabilistic structure. In this situation, the spectral estimation becomes a highly nonlinear optimization problem with many local minima. In this paper, we use the global search method of genetic algorithm (GA) to achieve a global optimal solution of this spectral estimation problem. From the simulation results, we have found that the performance is improved significantly if the probability of data missing is considered in the spectral estimation problem