Direction-of-Arrival Estimation Using Gaussian Process Interpolation

Abstract

Gaussian processes (GP’s) have been used to predict acoustic fields by interpolating under-sampled field observations. Using GP interpolation to predict fields is advantageous because of its ability to denoise measurements and for its prediction of likely field outcomes given a certain field coherence, or in GP terminology, a kernel. While there are many design options for a coherence function, in this study we focus on the radial basis function kernel for estimating the direction-of-arrival (DOA) of a plane wave impinging on a uniform linear array. We demonstrate that an array sampled with spacing larger than a half wavelength can benefit from GP interpolation, providing a smaller root mean squared error in comparison to the error of conventional beamforming for DOA estimation.