基于卷积神经网络的水下多目标方位估计方法

针对水下多目标方位估计问题,提出了一种利用卷积神经网络模型对目标声源进行方位估计的方法。该方法使用不等强度的声源数据进行训练并使用焦点损失函数作为训练损失函数。通过对阵列接收到的信号进行特征提取,使用焦点损失函数指导卷积神经网络训练,最终利用训练好的卷积神经网络模型进行目标方位估计。对不同模型参数的训练进行对比,结果表明所训练的卷积神经网络模型在较低信噪比条件下也能正确估计弱目标的方位。试验结果表明,与采用二元交叉熵损失函数的卷积神经网络模型相比,该方法对弱目标的方位估计能力更强,提高了方位估计的准确率。

Underwater multi-target azimuth estimation method based on convolutional neural network

To solve the problem of underwater multi-target azimuth estimation, a method to estimate the target sound source azimuth using the convolutional neural network (CNN) model is proposed. In this method, unequal intensity sound source data are used for training and the focal loss function is taken as the training loss function. Through the feature extraction of the signals received by the array, the focal loss function is used to guide the convolutional neural network training, and finally the trained convolutional neural network model is used to estimate the target azimuth. By comparison with the training results of different model parameters, it is shown that the trained convolutional neural network model can correctly estimate the azimuth of weak targets under the condition of low SNR. And, the experimental results show that in contrast with the convolutional neural network model using the binary cross-entropy loss function, the method in this paper has a stronger ability to estimate weak target azimuth and improves the estimation accuracy.