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

• 作 者: Yi-Ning Liu Hai-Qiang Niu Zheng-Lin Li
• 机 构: State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 1001
• 出 处: 中国物理快报（英文版）
• 发表日期: 2019年4期

Using deep convolutional neural networks as primary leaers and a deep neural network as meta-leaer,source ranging is solved as a regression problem with the ensemble leaing 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 30km,the mean absolute error of the range predictions is 1.0 km and the mean absolute percentage error is 7.9％.