Optimal direction finding with partially calibrated arrays in spatially correlated noise fields

The problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of possibly coherent signals buried in unknown correlated noise fields is shown to admit a neat solution under fairly general conditions. The ML estimator introduced in this paper (and referred to as MLE) is shown to be asymptotically equivalent to a subspace-based bearing estimator proposed by Wu and Wong (see IEEE Trans. Signal Processing, vol. 42, Sept. 1994) (called UNCLE and re-derived herein by a simpler approach than in the original work). A statistical analysis is performed, proving that the MLE and UNCLE methods are asymptotically equivalent and statistically efficient. In a simulation study, the methods are also found to possess very similar finite-sample properties