High accuracy estimation of multi-frequency signal parameters by improved phase linear regression

An improved phase regression approach for estimating the parameters of a multi-frequency signal from discrete samples corrupted by additive noise is presented. It efficiently estimates the signal frequency and phase by linear regression on the phase spectra of segmented signal blocks, and the signal amplitude directly from the discrete-time Fourier transform of the window function. The techniques of weighted spectral lines averaging and overlapped signal segmenting are introduced to improve the estimation accuracy. The expressions of the estimator variances are derived, and shown to almost reach the Cramer–Rao bounds. Numerical simulations are given to confirm the validity of the presented approach.