Signal restoration from data aliased in time

This paper deals with the problem of signal restoration from data aliased in time. The signal, which is in general noncausal, is split into a causal and an anticausal part. The causal part and the time-reversed anticausal part are then modeled as impulse responses of rational pole-zero models. The parameters of these models are then estimated by solving sets of overdetermined equations. The choice of model orders, i.e., number of poles and zeros of rational models, is also discussed. It is shown that if the aliasing period is large enough, there is sufficient information for all the parameters to be estimated. The special cases of a purely causal signal or a purely anticausal signal are discussed. Simulation results show that the signal can be recovered with excellent accuracy.