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Graduate Exam Abstract


Michael McCarron

M.S. Final

March 6, 2009, 9:00am

LSC

Adaptive Methods For Rapid Acoustic Transmission Loss Prediction In The Atmosphere


Abstract: An operationally adaptive (OA) system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this thesis. 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. In addition, new TL prediction frameworks for improving generalization of the expert neural network predictors are explored. The proposed OA system is tested on a synthetically generated acoustic data set for a wide range of geometric, source, and environmental conditions. The results show a significant improvement in both accuracy and reliability over a benchmark prediction system.

Adviser: Mahmood R. Azimi-Sadjadi
Co-Adviser: NA
Non-ECE Member: Tom Vonder Haar
Member 3: Edwin K. Chong
Addional Members: NA

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Program of Study: