Exact Unconditional ML Estimation of DOA
Masakiyo Suzuki, Haihua Chen · Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University) · 2009
This paper presents an exact formulation of Stochastic or Unconditional Maximum Likelihood (UML) estimation for directions-of-arrival (DOA) finding. In the previous formulation of UML estimation, an important condition is missing. That is the non-negative definiteness of the covariance matrix of signal components without additive noises. Because of the lack of the important condition, inadequate global solution appears in the solution space and global search fails to find adequate solution. We have derived an exact formulation including this important condition. Then the inadequate global solution disappears and global search finds adequate solution.