A novel high resolution parallel spectral estimation method for narrow-band signals

A high resolution parallel algorithm is proposed for estimating the spectrum of a narrow-band signal from a short data record. The algorithm is based on combining the nonparametric and parametric approaches, where the nonparametric approach is used to decompose the measurement data into an orthogonal set of components, and the parametric approach is used to estimate the model of these components in parallel. A fast Fourier transform (FFT) is used to decompose the signal. A singular value decomposition (SVD)-based linear predictive coding algorithm (LPCA) is used to obtain an autoregressive moving average (ARMA) model of the signal components. The FFT of the signal components is translated to the low-frequency region, and their inverse FFTs are decimated before estimating the ARMA model so as to separate the closely-spaced modes. The spectra of the estimates are translated back to their original location. The proposed algorithm is evaluated using simulation