Blind SNR estimation for limited time series

A blind SNR estimation method for discrete data is presented. The original noise-free data is assumed to follow a known signal model with an unknown attenuation. The SNR in noisy data is estimated using a polynomial fit obtained from the correlative characteristics between SNR and variance fractal dimension values. The bias and the “standard error” (root mean square error) of the estimator are used as performance measures. The simulated performance of the estimator for a specific signal model with real additive white Gaussian noise assumption is compared to that of a published decision-aided (nonblind) SNR estimator.