Blind beamforming for non-gaussian signals

TLDR

The paper applies blind identification to beamforming. The study presents a computationally efficient technique for blind estimation of directional vectors via joint diagonalisation of fourth‑order cumulant matrices. The method uses estimates of directional vectors derived from blind identification, exploiting source independence through fourth‑order cumulants and joint diagonalisation. Using blind‑estimated directional vectors, beamforming becomes robust to array deformations, wave‑front distortion, and pointing errors without calibration, and can even outperform informed beamformers under realistic conditions.

Abstract

The paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resort to their hypothesised value. By using estimates of the directional vectors obtained via blind identification, i.e. without knowing the array manifold, beamforming is made robust with respect to array deformations, distortion of the wave front, pointing errors etc., so that neither array calibration nor physical modelling is necessary. Rather suprisingly, 'blind beamformers' may outperform 'informed beamformers' in a plausible range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption on which blind identification relies is the statistical independence of the sources, which is exploited using fourth-order cumulants. A computationally efficient technique is presented for the blind estimation of directional vectors, based on joint diagonalisation of fourth-order cumulant matrices; its implementation is described, and its performance is investigated by numerical experiments.