Rapid acoustic transmission loss prediction using an operationally adaptive system

Authors:
Michael McCarron;Mahmood R. Azimi-Sadjadi;Michael Mungiole;David Marlin
Affiliations:
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO;Army Research Laboratory, Adelphi, MD;Army Research Laboratory, White Sands Missil Range, NM
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 1
Cited 0

2007 Special Issue: Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

New fusion methods for an operationally adaptive (OA) system for prediction of acoustic transmission loss (TL) in the atmosphere are developed in this paper. The OA system uses expert neural network predictors, each corresponding to a specific range of source elevation. The outputs of the expert predictors are combined using two new nonlinear fusion methods. Using this prediction methodology the computational intractability of traditional acoustic propagation models is eliminated. The proposed fusion methods are tested on a synthetically generated acoustic data set for a wide range of geometric, source, and environmental conditions.