Abstract
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 bearing estimation. Two general forms for calculating SST matrices are presented, and the rotational signal-subspace (RSS) focusing matrices proposed by H. Hung and M. Kaveh (1988) are shown to be a special case of the SST matrices. An efficient procedure for computing a subset of the SST matrices, utilizing Householder transformations, is presented. The procedure reduces the computational load by a factor of 10, compared with that for the RSS matrices. The application of MUSIC to the coherently combined covariance matrix is also discussed, and Monte Carlo simulations comparing the performance of Householder SST matrices and RSS matrices are performed. >
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