We consider the problem of forecasting realized variance measures. These measures are highly persistent, but also noisy estimates of the underlying integrated variance. Recently, Bollerslev, Patton and Quaedvlieg (2016, Journal of Econometrics, 192, 1-18) exploited this fact to extend the commonly used Heterogeneous Autoregressive (HAR) by letting the model parameters vary over time depending on estimated measurement errors. We propose an alternative specification that allows the autoregressive parameter of the HAR model for volatilities to be driven by a latent Gaussian autoregressive process that may depend on the estimated measurement error. The model is estimated using the Kalman filter. Our analysis considers realized volatilities of 40 stocks from the S&P 500 for three different observation frequencies. Our preferred model provides a better model fit and generates superior forecasts. It consistently outperforms the competing models in terms of different loss functions and for various subsamples of the forecasting period.