Large dimensional random matrix theory for signal detection and estimation in array processing

This paper brings into play elements of the spectral theory of such matrices and demonstrates their relevance to source detection and bearing estimation in problems with sizable arrays. These results are applied to the sample spatial covariance matrix, R, of the sensed data. It is seen that detection can be achieved with a sample size considerably less than that required by conventional approaches. It is argued that more accurate estimates of direction of arrival can be obtained by constraining R to be consistent with various a priori constraints including those arising from large dimensional random matrix theory. A set theoretic formalism is used for this feasibility problem. Unsolved issues are discussed.<>