Optimal environmental estimation with ocean ambient noise.

Abstract

This article presents an asymptotically optimal technique for estimating environmental parameters from ocean ambient noise. Noise from wind and breaking waves propagates through the water column and reflects off the bottom over a wide range of angles and frequencies and, in doing so, imparts information about the environment to the noise covariance matrix for a receiver array. Most environmental estimation techniques focus on spatial filtering methods aimed at recovering the vertical noise directionality. However, an often overlooked fact is that the noise covariance matrix fully characterizes the probability density function of each snapshot, which forms the basis for an information-theoretic approach. In this light, it is possible to obtain the theoretical bounds on optimal estimator performance while also providing a basis for assessing the utility of different parameterization schemes. Most importantly, it provides a natural definition for a maximum likelihood estimator that meets the optimal bounds in an asymptotic sense. This technique outperforms beamforming-based methods by a significant margin. It also remains unbiased in the presence of strong white noise, is tolerant to array tilt, can operate beyond the array design frequency, but does suffer greater sensitivity to model mismatch. These trade-offs are explored with simulations and analyses of experimental data.