Direction of arrival estimation using Gaussian process interpolation

Abstract

Gaussian processes (GP) have been used to predict acoustic fields by interpolating under-sampled field observations. Using GP interpolation to predict fields is advantageous due to 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 examine using the radial basis function kernel, the physically based plane wave kernel, and a composition of plane wave kernels representing a certain angular interval of directions. The composite kernel is relevant in ocean acoustics where it is often the case that arrivals can only be within a narrow direction of arrival. We demonstrate that an array sampled with spacing larger than a half wavelength can benefit from GP interpolation, giving less root mean squared error.