  In this brief paper, we present a simple approach to estimate the variance of
measurement noise with time-varying 1-D signals. The proposed approach exploits
the relationship between the noise variance and the variance of the prediction
errors (or innovation sequence) of a linear estimator, the idea that was
pioneered by [9] and [2]. Compared with the classic and more recent methods in
the same category, the proposed method can render more robust estimates with
the presence of unmodelled dynamics and outliers in the measurement.
