Rapid acoustic transmission loss prediction using an operationally adaptive system

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.