Periodic signal modeling using the gradient-based iterative estimation algorithm

This paper derives a new method for periodic signal modeling. It models the periodic signal as a function of the trigonometric functional extension (Fourier Series). The Fourier Series of the periodic signal are parameterized by fundamental frequency and unknown coefficients and a gradient-based iterative identification algorithm is developed for estimating the unknown parameters. The proposed algorithms are able to estimate the fundamental frequency and the unknown coefficients simultaneously. Finally, the simulation results indicate that the proposed algorithm are effective.