An efficient signal subspace algorithm for source localization in noise fields with unknown covariance

The authors present a covariance differencing algorithm for bearing estimation in situations where the noise covariance matrix is unknown. Conventional covariance differencing methods acquire a set of vectors which are orthogonal to the direction vectors by obtaining an eigenvalue decomposition of the difference of the covariance matrices of two measurements of the array. Eigendecomposition algorithms, however, involve a considerable computational burden. The authors also consider a procedure which does not require eigenvalue decomposition and is thus computationally more efficient. Results of simulation studies are included to show that the proposed approach performs nearly as well as the more conventional covariance differencing techniques in terms of signal resolution and estimation error