I would like to apply Kalman smoothing to a series of data sampled at irregular time points. There is a claim on Stack Exchange that "For irregular spaced time series it's easy to construct a Kalman filter", but I haven't been able to find any literature that specifically addresses this.
In my situation, I'd like to use a simple exponential covariance relationship to reflect the idea that the underlying continuous process is evolving as a linear dynamical system from which we irregularly receive samples.
So: is it simply OK to apply a Kalman filter with the "predict" step using a transition model and a process noise model whose "amplitude" depend on the amount of time that has elapsed since the last measurement?