  Compressed sensing (CS) with prior information concerns the problem of
reconstructing a sparse signal with the aid of a similar signal which is known
beforehand. We consider a new approach to integrate the prior information into
CS via maximizing the correlation between the prior knowledge and the desired
signal. We then present a geometric analysis for the proposed method under
sub-Gaussian measurements. Our results reveal that if the prior information is
good enough, then the proposed approach can improve the performance of the
standard CS. Simulations are provided to verify our results.
