Research on Underwater Source Localization Method Based on Multi-task Neural Networks
In order to solve the problems of large sensor array,difficult deployment and environmental mismatch of traditional underwater passive positioning algorithm,a method of underwater source localization based on multi-task neural network is pro-posed.By simulating the shallow sea acoustic field dataset,using the relative time difference of the acoustic signal to reach each measurement sensor,combined with the deep learning method,the MTL-Attention-UNet neural network model is designed on the basis of the multi-task convolutional neural network MTL-CNN(Multi-task Convolutional Neural Network)and Attention-UNet structure,and the distance and depth of the underwater seismic source are jointly estimated.The simulation results show that the av-erage absolute error of positioning the underwater source by MTL-Attention-UNet model is smaller than that of the MTL-CNN net-work model,and the positioning performance is better.
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