Fast estimation method of harmonic parameters based on sparse iterative covariance matrix

Aiming at the limitations of sparse iterative covariance estimation (SPICE) method had low estimation?accuracy of power spectrum estimation and high computational complexity, a fast estimation method based on sparse iterative covariance estimation for power spectrum and frequency parameters of harmonic signals had been proposed. The method combing the asymptotically minimum variance criterion and the fast discrete Fourier transform to estimate power spectrum parameters by a quickly iteration calibration. Specifically, first the initial estimation of power spectrum and frequency parameters was obtained through the SPICE algorithm. Then, according to the asymptotic minimum variance criterion, the iterative correction expression of power spectrum parameters was obtained. Finally, the power spectrum parameters could be iteratively corrected by the spectrum correction expression. For the purpose of improving the calculation efficiency of the algorithm, the Toeplitz structure of the covariance matrix of the observation data and the exponential form of the steering vector were used, and the (Gohberg-Semencul, G-S) decomposition of the covariance matrix was adopted. The matrix and vector multiply was solved by fast Fourier transform, thus greatly reducing the calculation time of parameter estimation. Simulation experiments are used to verify that the proposed algorithm has high estimation accuracy for harmonic power spectrum and frequency parameters, and the computational complexity is low.