Optimal filter bank reconstruction of periodically undersampled signals

A multirate filter bank model is considered for reconstruction of periodically sampled signals. In contrast to many previous methods which considered perfect reconstruction of deterministic signals, this approach uses a known discrete-time cyclostationary signal model to find a minimum mean-squared error reconstruction solution. A primary advantage of this approach is that it does not require a minimum sampling density, allowing optimal solutions to be determined in cases of undersampling. This allows for consideration of a wide range of generalized sampling problems under a single framework. An example, with simulation results, is presented.