Generalization and analysis of the conventional beamformer for localization of spatially distributed sources

In this paper, we generalize the point source-based conventional beamformer (CBF) to localization of multiple distributed sources that appear in sensor array processing. A distributed source is commonly parameterized by its mean angle and spatial spread. The generalized CBF uses the principal eigenvector of the parameterized signal covariance matrix as its optimal weight vector, which is also shown to be a matched filter. The desired parameter estimates are taken as the peaks of the generalized 2-dimensional beamforming spectrum. Further, the performance of the algorithm is compared numerically to a generalized Capon estimator [1]. Finally, an asymptotic performance analysis of the proposed algorithm is provided and numerically verified.