title: An operationally adaptive system for rapid acoustic transmission loss prediction.
abstract: An operationally adaptive (OA) system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this paper. This system uses expert neural network predictors, each corresponding to a specific range of source elevation. The outputs of the expert predictors are combined using a weighting mechanism and a nonlinear fusion system. Using this prediction methodology the computational intractability of traditional acoustic propagation models is eliminated. The proposed system is tested on a synthetically generated acoustic data set for a wide range of geometric, source, environmental, and operational conditions. The results show a significant improvement in both accuracy and reliability over a benchmark prediction system.
authors.affiliation: Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA. michael.v.mccarron@gmail.com
authors.forename: Michael
authors.initials: M
authors.lastname: McCarron
authors.forename: Mahmood R
authors.initials: MR
authors.lastname: Azimi-Sadjadi
authors.forename: Michael
authors.initials: M
authors.lastname: Mungiole
mesh_terms: D000162:Acoustics; D001272:Atmosphere; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012984:Software