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The performance of nonlinear power spectral estimation methods is studied from the viewpoint of resolution. The methods considered are the maximum entropy method, the maximum likelihood method, the least-squares method, Prony's energy spectral-density method, the singular-value decomposition technique and the autoregressive moving-average power spectral-estimation method. The data model used comprises two sinusoidal signals of equal amplitudes immersed in white Gaussian noise. The minimum resolution that can be obtained for all the power spectral-estimation methods considered has been studied for different data lengths and signal-to-noise ratios and the results are tabulated.