SPATIAL-SPECTRUM ESTIMATION IN A LOCATION SECTOR

In this paper we consider multiple narrow-band source localization using arbitrarily configured arrays and spatial-spectrum estimation. We describe a new eigenspace based approach which employs projections onto a particular vector or vector set in the estimated noise-only subspace. This vector or vector set is one which is, in some sense, closest to the section of the array manifold corresponding to a source location sector of interest. Several CLOSEST vector estimators are developed by employing different measures of closeness. We discuss the relative advantages of each estimator. The novelty and significance of this paper is twofold. First, CLOSEST is a new full-dimensional element-space approach to spatial-spectrum estimation which has important performance advantages relative to pertinent established spatial-spectrum estimators. It incorporates a priori knowledge of the array manifold over a location sector of interest to provide SNR spectral-resolution thresholds which are lower than those of MIN-NORM (for some arrays, substantially lower), while generating location estimates with variances observed to be comparable to those of location estimates from MUSIC. Second, in this paper we establish relationships between the CLOSEST approach and several established approaches to spatial-spectrum estimation. For a linear equispaced array, MIN-NORM is shown to be a special case of the CLOSEST approach—one which is based on projection onto a noise-only subspace vector which is close to the array manifold over the entire FOV. By establishing a relationship between one of the new CLOSEST estimators and spatial-spectrum estimation in a reduced-dimension beam-space, we reveal the mechanism behind which (as recently observed) beam-space processing with MUSIC provides spectral-resolution thresholds which are lower than those of MUSIC and MIN-NORM in element-space. © 1990 IEEE