  This paper deals with the problem of sparse recovery often found in
compressive sensing applications exploiting a priori knowledge. In particular,
we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT)
algorithm that exploits information about the probabilities of nonzero entries.
We also develop a strategy to update the probabilities using a recursive
KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation
results illustrate and compare the performance of the proposed and existing
algorithms.
