Yu Qiankun, Zhang Wen, Zhu Min, Shi Jian, Liu Yan, Liu Shuo
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.
J Acoust Soc Am. 2024 Jun 1;155(6):3606-3614. doi: 10.1121/10.0026221.
Surface and underwater (S/U) acoustic targets recognition is an important application of passive sonar. It is difficult to distinguish them due to the mixture of underwater target radiation noise and marine environmental noise. In previous studies, although using a single hydrophone was able to identify S/U acoustic targets, there were still a few hydrophones that had poor accuracy. In this paper, S/U acoustic targets recognition using two hydrophones based on Gradient Boosting Decision Tree is proposed, and it is first found out as high as 100% accuracy could be achieved with the implementation of SACLANT 1993 data. The real experimental data are always rare and insufficient. The big training dataset is generated using environmental information by acoustic model named KRAKEN. Simulation and experimental data used in the model are heterogeneous, and the differences between these two kinds of data are assimilated by using vertical linear array feature extraction method. The model realizes the recognition of S/U acoustic targets based on channel information besides source spectrum information. By using the combination of two hydrophones, the surface and underwater targets recognition accuracy reached 1 and 0.9384, while they are only 0.4715 and 0.5620 using a single hydrophone, respectively.
水面和水下（S/U）声学目标识别是被动声纳的一项重要应用。由于水下目标辐射噪声和海洋环境噪声的混合，很难区分它们。在以往的研究中，虽然使用单个水听器能够识别S/U声学目标，但仍有一些水听器的精度较差。本文提出了基于梯度提升决策树的双水听器S/U声学目标识别方法，首次发现利用SACLANT 1993数据可实现高达100%的准确率。实际实验数据总是稀少且不足。利用名为KRAKEN的声学模型通过环境信息生成大型训练数据集。模型中使用的模拟数据和实验数据是异构的，通过垂直线性阵列特征提取方法来同化这两种数据之间的差异。该模型除了基于源谱信息外，还实现了基于信道信息的S/U声学目标识别。通过使用两个水听器的组合，水面和水下目标识别准确率分别达到了1和0.9384，而使用单个水听器时分别仅为0.4715和0.5620。
