Fast missing-data IAA by low rank completion

The adaptive spectral estimation method IAA provides better performance than the periodogram at the cost of higher computational complexity. Current fast IAA algorithms reduce the computational complexity using Toeplitz/Vandermonde structures, but are not efficient for missing data cases when the number of missing samples is small. We considerably reduce the computational complexity compared to the state-of-the-art by using a low rank completion to transform the problem to a Toeplitz/Vandermonde structured problem.