ON FOCUSING MATRICES FOR WIDE-BAND ARRAY-PROCESSING

In this paper a general class of transformation matrices for coherent signal subspace processing is presented. These signal-subspace transformation (SST) matrices are shown to generate a sufficient statistic for maximum-likelihood (ML) bearing estimation. Two general forms for calculating SST matrices are presented, and the rotational signal-subspace (RSS) focusing matrices proposed by Hung and Kaveh are shown to be a special case of the SST matrices. An efficient computation procedure of a subset of the SST matrices, utilizing House-holder transformations, is presented. The proposed procedure reduces the computational load by a factor of 10, compared with that of the RSS matrices. The application of MUSIC to the coherently combined covariance matrix is also discussed, and Monte Carlo simulations comparing the performance of House-holder SST (HSST) matrices and RSS matrices are performed.