Source Ranging Using Ensemble Convolutional Networks in the Direct Zone of Deep Water

【摘要】 Using deep convolutional neural networks as primary learners and a deep neural network as meta-learner, source ranging is solved as a regression problem with the ensemble learning method. Simulated acoustic data from the acoustic propagation model are used as the training data. Real data from an experiment in the South China Sea are used as the test data to demonstrate the performance. The results indicate that in the direct zone of deep water, signals received by a very deep receiver can be used to estimate the range of underwater sound source.Within 30 km, the mean absolute error of the range predictions is 1.0 km and the mean absolute percentage error is 7.9%.更多还原

【Abstract】 Using deep convolutional neural networks as primary learners and a deep neural network as meta-learner, source ranging is solved as a regression problem with the ensemble learning method. Simulated acoustic data from the acoustic propagation model are used as the training data. Real data from an experiment in the South China Sea are used as the test data to demonstrate the performance. The results indicate that in the direct zone of deep water, signals received by a very deep receiver can be used to estimate the range of underwater sound source.Within 30 km, the mean absolute error of the range predictions is 1.0 km and the mean absolute percentage error is 7.9%.更多还原