A new method for signal detection and estimation using the eigenstructure of the covariance difference

High-resolution eigenstructure methods developed over the last several years for the passive detection of signals in the far field of a receiving array suffer from the defect of requiring the noise to be white or having a known covariance matrix. This defect can be removed by covariance-matrix subtraction which only requires that the noise be isotropic under rotation of the array. Subtraction results in a received-signal covariance matrix characterized by M-2p zero eigenvalues. There are several likelihood-ratio tests that take advantage of this eigenvalue pattern to determine the number of signal sources. The Cramer-Rao bound for errors in direction estimation indicates that covariance subtraction results in about the same error as other methods using the same amount of received data.