Environmentally Adaptive Sonar Control in a Tactical Setting
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Automatic environmentally adaptive sonar control in littoral regions characterized by high spatial/temporal acoustic variability is an important operational need. An acoustic model-based sonar conroller requires an accurate model of how the sonar would perform in the current environment while in any of its possible configurations. Since high-fidelity acoustic models are computationally intensive, and finding the optimal sonar mode may require a large number of these model runs, such a controller may not be able to provide optimal line-up solutions in tactically useful time frames. We have explored a method of statistically characterizing a given operations area, generating a large ensemble of acoustic model runs, and training specialized artificial neural networks to emulate acoustic model input/output relationships. The neural networks reproduce the acoustic model outputs to a good degree of accuracy in a small fraction of the compute time needed for one of the original model runs. In this paper, the neural network training method is described, examples of neural network performance are given, and an example of controller solutions in a variable environment are presented.
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