An Operationally Adaptive System for Rapid Acoustic Transmission Loss Prediction

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 performance-aware weighting mechanism and a nonlinear fusion system. Using this prediction methodology the computational intractability of traditional acoustic models is eliminated. The proposed system is tested on a synthetically generated acoustic data set for a wide range of geometric, source, and environmental conditions.