For a narrowband signal, an oscillating interference pattern is formed with a target's moving when receiving at the bottom of the sea. In this Letter, the interference pattern of a narrowband source is observed using a single vector sensor (SVS). A passive depth estimation method employing a SVS is proposed. This approach processes the signals after the adaptive line enhancing and extracts the vector intensity, which oscillates periodically with the vertical azimuth. The passive estimation is achieved based on the Fourier-transform relationship between the depth and interference period. The simulation and sea experiment verify this method.

In view of the non-Gaussian of ocean ambient noise, the  stable distribution is applied to the statistical modelling. Firstly, the one-to-one correspondence between the four parameters of stable distribution and the sample mean, variance, skewness and kurtosis are established according to physical meaning. Then, numerical simulations are conducted to analyze the suitability of stable distribution for non-Gaussian ambient noise. In the case of white noise interference, noise is divided into Gaussian state, leptokurtic, and platykurtic separately. The parameters of stable distribution are estimated by the sample quantile and characteristic function method jointly. The simulation results show that, in the Gaussian state,  stable distribution is equivalent to normal distribution. As for leptokurtic distribution, stable distribution is much better than normal distribution, indicating absolute predominance in impulse-like data modeling. But it is not adaptive for low kurtosis state because its characteristic exponent can’t be bigger than two. Finally, the result is verified by ambient noise collected in three environmental conditions, such as quiet ambient noise, airgun interference noise and ship noise. In all three cases,  stable distribution shows good adaptability and accuracy, especially for the airgun dataset it is far superior to normal distribution.

Underwater noise classification is of great significance for identifying ships as well as other vehicles. Moreover, it is helpful in ensuring a marine habitat-friendly, noise-free ocean environment. But a challenge we are facing is the small-sized underwater noise samples. Because noise is influenced by multiple sources, it is often difficult to determine and label which source or which two sources are dominant. At present, research to solve the problem is focused on noise image processing or advanced computer technology without starting with the noise generation mechanism and modeling. Here, a typical underwater noise generation model (UNGM) is established to augment noise samples. It is established by generating noise with certain kurtosis according to the spectral and statistical characteristics of the actual noise and filter design. In addition, an underwater noise classification model is developed based on UNGM and convolutional neural networks (CNN). Then the UNGM-CNN-based model is used to classify nine types of typical underwater noise, with either the 1/3 octave noise spectrum level (NSL) or power spectral density (PSD) as the input features. The results show that it is effective in improving classification accuracy. Specifically, it increases the classification accuracy by 1.59%, from 98.27% to 99.86%, and by 2.44%, from 97.45% to 99.89%, when the NSL and PSD are used as the input features, respectively. Additionally, the UNGM-CNN-based method appreciably improves macro-precision and macro-recall by approximately 0.87% and 0.83%, respectively, compared to the CNN-based method. These results demonstrate the effectiveness of the UNGM established in noise classification with small-sized samples.

海洋环境噪声场中包含了海洋中的诸多信息, 海底地声参数是影响海洋环境噪声场空间分布的主要因素之一. 对于不同的海底分层结构, 海底反射损失会根据沉积层厚度和各层声速呈现出不同的临界角和干涉条纹结构. 本文利用Harrison能流理论, 从理想反射系数出发, 分别考虑了声速、密度、衰减系数、沉积层厚度等几种参数对无沉积层和单层沉积层中反射系数的影响, 并对单层沉积层海底的反射系数进行了化简, 结合互易原理解释了反射损失条纹结构的形成机理. 中国黄海某海区试验结果表明, 利用海洋环境噪声空间方向谱获得的海底反射损失, 可以提取海底反射临界角和干涉条纹信息, 由此可估计出海底分层结构、声速和沉积层厚度等海底参数信息.

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.

When solving traditional underwater problems, the boundary condition is always used to calculate the sound field. In practice, however, it is hard to get the boundary conditions of the seabed. So geoacoustics inversion is needed to acquire the parameters of the seabed. In this paper, a method estimating seabed parameters by using the spatial characteristics of ocean ambient noise is demonstrated without using matched-field processing. For the reason of the limit of the resolution of conventional beamforming (CBF), a method of synthetic array processing (SAP) is used because of some characters of cross-spectrum density matrix (CSDM). The result shows that the method of synthetic array processing enhanced the resolution of critical angle to some degree. By comparing the true bottomloss calculated by OASR, the result of traditional beamforming and the synthetic array processing, the result of synthetic array processing is closer to the true bottomloss than the result of traditional beamforming. After ensuring a range of critical angle, the sound speed of the seabed can be estimated by using Snell law. And then, an experimental data collected in Qingdao, China, 2016 is used to prove the validity of the method of synthetic array processing and estimate the local seabed parameters.