Data reduction in spatially colored noise using a virtual uniform linear array

This paper focuses on beamspace DOA estimation in colored noise. We present a beamspace transformation that preserves the Cramer Rao bounds for the parameters of interest, and at the same time interpolates the true array manifold in such a way that a virtual uniform linear array is obtained in beamspace. Besides the computational savings offered by the dimensionality reduction, the virtual array enables the use of computationally simple algorithms such as root-MUSIC or ESPRIT