Improvement on the estimation of covariance matrices by incorporating crosscorrelations

Addresses the problem of improving the estimate of a covariance matrix from one set of multivariate random processes when there exist nonzero crosscorrelations with another set of random processes. The improvement is obtained by linearly combining the first set´s sample covariance matrix with covariance matrices predicted via the crosscorrelations. The superiority of the proposed method is demonstrated by an application to spatial smoothing for the direction-of-arrival (DOA) estimation of coherent narrowband signals using a uniform linear array