A robust spectral estimation by modeling an estimated autocovariance with an ARMA model

The development, analysis, simulation results, and comparison of techniques for estimating spectral peaks of stationary signals in environments with low signal-to-noise ratio are discussed. Unlike traditional parametric methods, the proposed method estimates parameters of a model which best approximates the estimated autocovariance lag rather than the received signal. This technique is studied and evaluated in three different respects: using a performance index in the spectral domain, suppression of the moving-average (MA) portion and in terms of effective signal-to-noise ratio. This estimation technique demonstrates outstanding robustness and resolution for estimating both spectral peaks and amplitudes of multiple sinusoids embedded in white Gaussian noise, compared to traditional methods. When used in conjunction with Cadzow´s autoregressive moving-average (ARMA) method using singular value decomposition (SVD), the technique extracts frequencies and amplitudes of existing sinusoids down to -17 dB while the ARMA method alone achieves only -10 dB on average