Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.
Key Laboratory of Underwater Acoustics Environment, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2021 Apr 29;21(9):3109. doi: 10.3390/s21093109.
A modified convolutional neural network (CNN) is proposed to enhance the reliability of source ranging based on acoustic field data received by a vertical array. Compared to the traditional method, the output layer is modified by outputting Gauss regression sequences, expressed using a Gaussian probability distribution form centered on the actual distance. The processed results of deep-sea experimental data confirmed that the ranging performance of the CNN with a Gauss regression output was better than that using single regression and classification outputs. The mean relative error between the predicted distance and the actual value was ~2.77%, and the positioning accuracy with 10% and 5% error was 99.56% and 90.14%, respectively.
提出了一种改进的卷积神经网络（CNN），用于增强基于垂直阵接收的声场数据的源测距可靠性。与传统方法相比，通过输出高斯回归序列来修改输出层，该序列采用以实际距离为中心的高斯概率分布形式表示。深海实验数据的处理结果证实，具有高斯回归输出的 CNN 的测距性能优于使用单一回归和分类输出的 CNN。预测距离与实际值之间的平均相对误差约为 2.77%，误差为 10%和 5%时的定位精度分别为 99.56%和 90.14%。
