Vectorized Hankel Lift: A Convex Approach for Blind Super-Resolution of Point Sources

We consider the problem of resolving $r$ point sources from notation="LaTeX">$n$ samples at low end spectrum when spread functions (PSFs) are not known. Assuming that PSFs lie in dimensional subspace (let notation="LaTeX">$s$ denote dimension), we can formulate it as a matrix recovery problem, followed by location estimation. By exploiting rank structure vectorized Hankel associated with target matrix, convex approach called Vectorized Lift is proposed for recovery. It shown notation="LaTeX">$n\gtrsim rs\log ^{4} n$ sufficient to achieve exact For retrieval applying single snapshot MUSIC method within lift framework corresponds spatial smoothing technique improve performance MMV direction-of-arrival (DOA)