A maximum likelihood score-function with optimal eigenvector weights for bearing estimation

A high-resolution estimator is derived from maximum likelihood (ML) principles, solving for values of bearing parameters for which the partial of the likelihood functions with respect to the bearing parameter (the score function) is zero. The estimator is shown to give optimal weights to the noise-space eigenvectors from the point of view of maximizing the slope of the score function at the solution point. Simulations show that this algorithm gives greater accuracy than minimum norm (MN) near-single sources. It is shown that MN and MUSIC can be interpreted as a particular windowing of ML