Adaptive signal reconstruction

An adaptive filter which reconstructs a continuous signal from its samples is described. This filter is based on the minimum mean-square-error reconstruction filter, assuming an all-pole model for the sampled spectral density of the input signal. The use of this model leads to two important simplifications. First, simple linear regression can be used to identify the unknown parameters of the signal spectral density. Second, the resulting filter has an impulse response which is of finite duration. These simplifications lead to an adaptive filter which is at the same time both generally applicable and easily implemented on a digital or hybrid computer. Experiments with both deterministic and random inputs are described which show that the adaptive filter yields significant improvement over a linear point connector or other commonly used reconstructors with relatively low order models and with relatively short identification times.