Convolutional Beamspace for Array Signal Processing

Abstract

A new type of beamspace for array processing is introduced called convolutional beamspace. It enjoys the advantages of traditional beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike traditional beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without any overhead of preparation, as the Vandermonde structure and the shift-invariance are preserved under the transformation. The method produces more accurate DOA estimates than traditional beamspace methods, and for correlated sources it produces better estimates than element-space methods.

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