Causal reconstruction kernels for consistent signal recovery

This paper derives causal reconstruction kernels which allow for a consistent signal recovery of the past signal component from the past signal samples only. Our approach is based on classical Hilbert space methods of signal sampling and recovery. The causal reconstruction kernels are obtained as the causal dual frame for a given sequence of sampling functions. The proposed methodology is illustrated by a numerical example.