Temporally weighting a time varying noise field to improve Green function retrieval

Abstract

The authors consider the retrieval of Green functions G from the correlations of non-stationary non-fully diffuse noise incident on an array of sensors. Multiple schemes are proposed for optimizing the time-varying weights with which correlations may be stacked. Using noise records created by direct numerical simulation of waves in a two-dimensional multiply scattering medium, cases are shown in which conventional stacking does a poor job and for which the proposed schemes substantially improve the recovered G, rendering it more causal and/or more symmetric, and more similar to the actual G. It is found that the schemes choose weights such that the effective incident intensity distribution is closer to isotropic.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2018
Source ID
10.1121/1.5043406

Entities

People

  • John Y Yoritomo
  • Richard L. Weaver

Organizations

  • Air Force Research Laboratory
  • University of Illinois Urbana–Champaign

Tags

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Computational Modeling and Simulation
  • Neural Network Machine Learning.