  In this work, we investigate the problem of simultaneous blind demixing and
super-resolution. Leveraging the subspace assumption regarding unknown point
spread functions, this problem can be reformulated as a low-rank matrix
demixing problem. We propose a convex recovery approach that utilizes the
low-rank structure of each vectorized Hankel matrix associated with the target
matrix. Our analysis reveals that for achieving exact recovery, the number of
samples needs to satisfy the condition $n\gtrsim Ksr \log (sn)$. Empirical
evaluations demonstrate the recovery capabilities and the computational
efficiency of the convex method.
