A state-space approach to optimum postfiltering of sampled data

In this paper, we apply state-space techniques to the problem of reconstructing a random continuous-time waveform from its discrete-time samples. The optimal zero-lag filter that accomplishes this is well known, but to our knowledge the smoothing problem has not been previously considered in this context. We develop smoothing algorithms in the mixed-time framework appropriate to the interpolation problem, and then compare our results with those obtained previously in discrete time. Our results are related to those previously obtained in a simple and intuitive way, and the required computations are straightforward modifications of those described previously for purely discrete and purely continuous time.