  This paper introduces a subspace method for the estimation of an array
covariance matrix. It is shown that when the received signals are uncorrelated,
the true array covariance matrices lie in a specific subspace whose dimension
is typically much smaller than the dimension of the full space. Based on this
idea, a subspace based covariance matrix estimator is proposed. The estimator
is obtained as a solution to a semi-definite convex optimization problem. While
the optimization problem has no closed-form solution, a nearly optimal
closed-form solution is proposed making it easy to implement. In comparison to
the conventional approaches, the proposed method yields higher estimation
accuracy because it eliminates the estimation error which does not lie in the
subspace of the true covariance matrices. The numerical examples indicate that
the proposed covariance matrix estimator can significantly improve the
estimation quality of the covariance matrix.
