A novel data-supported optimization technique for maximum likelihood (ML) d
irection-of-arrival estimation is proposed. The essence of our approach is
to optimize the likelihood function at certain data-supported points obtain
ed by a resampled root-MUSIC procedure. These points are shown to comprise
a small but representative subset of all possible searching points and cont
ain enough information for solving the ML problem.