Broadband DOA estimation using "Spatial-Only" modeling of array data

Most of the existing techniques for DOA estimation of broadband sources use both spatial and temporal modeling. This may lead to increased complexity besides a large algorithmic delay. We propose a technique that employs only spatial information in the form of a single spatial array covariance matrix. Assuming the source to have an ideal bandpass power spectral density, we formulate two subspace-based search functions for the estimation of the DOAs of broadband sources. One of these employs a multidimensional search in the parameter space, whereas the other requires a MUSIC like one-dimensional (1-D) search. The multidimensional cost function is shown to be consistent, yields performance close to the Cramer-Rao (CR) bound, and is insensitive to correlation between sources. Both the proposed methods are shown to be robust to deviations from the assumption of ideal bandpass power spectral density used in their formulation.