A deep transfer learning for underwater source ranging is proposed, which migrates the predictive ability obtained from synthetic environment (source domain) into an experimental sea area (target domain). A deep neural network is first trained on large synthetic datasets generated from historical environmental data, and then part of the neural network is refined on collected data set for source ranging. Its performance is tested on a deep-sea experiment through comparing with convolutional neural networks of different training datasets. Data processing results demonstrate that the ranging accuracy is considerably improved by the proposed method, which can be easily adapted for related areas.

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.

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.

Various line array configurations are evaluated for the source localization performance based on the analysis of mode decomposition matrix in this paper. The guideline of array shape design focuses on improving the localization performance of matched filed processing, meanwhile reducing the difficulty of deploying equipment in practical experiments. In the shallow water environment, when the environment is well known, the source localization result can be obtained by matched field processing algorithms effectively, but the source localization performance is affected by the array parameters, such as array length, the number of sensors, and the configurations of various horizontal and vertical line arrays. The modal decomposition method provides a useful insight into the questions of how many modes are needed and how to design the array to resolve the modes. Therefore, the method of utilizing a normal mode acoustic propagation model to decompose mode is proposed by vertical line array, horizontal line array and combined array respectively. Then we can evaluate the source localization performance of various line array configurations by studying the characteristic of normal mode decomposition matrix, thus establishing a qualitative or even quantitative relationship between each other. The more the normal mode decomposition matrix tends to be diagonalized, the better performance of line array localization will be obtained. Simulation results show that the localization performance of matched field processing with the combined arrays will be severely degraded when the mode amplitudes cannot be accurately deduced by one of the sub-arrays. Considering the requirements for the practical experiments and various environments, the source localization performance of short vertical line array and combined array are mainly discussed in this paper. The combined array can increase the azimuth and depth information of the source and realize three-dimensional target detection while the vertical array provides range-depth information and the horizontal array provides bearing information. Simulation result indicates that the design guidelines based on the normal mode decomposition are appropriate for arrays employed for matched filed processing. Meanwhile, the combined arrays perform better than the short vertical array, which is benefited by the horizontal array's suppressing the side lobes, which leads the ratio of peak to sidelobe to increase, and thus improving the location accuracy. The values of localization accuracy of combined arrays are all above 90% according to the simulation experiment. Take the practical application into account, the combined array is undoubtedly a compromise choice for the localization performance and the test complexity.

The relationship between modal elevation angle and the relative arrival time between modes, derived from exploiting modal dispersion, provides source information that is less susceptible to environmental influences. However, the standard method based on modal dispersion has limitations for application. To overcome this, we propose a hybrid method for passive source ranging of low-frequency underwater acoustic-pulse signals in a range-independent shallow-water waveguide. Our method leverages deep learning, utilizing the intermediate results from the standard method as inputs, and short-time conventional beamforming to transform signals received by a vertical line array into a beam-time-domain sound-intensity map. The source range is estimated using an attention-based regression model with a ResNet backbone that has been trained on the beam-time-domain sound-intensity map. Our experimental results demonstrate the superiority of the proposed method, with a mean relative-error reduction of 71%, mean root-squared error reduction of 2.25 km, and an accuracy of 85%, compared to matched-field processing.

The sound speed profile (SSP) in water column is highly variable in time and space and can not be accurately known among measurement periods in coastal water. These factors will lead to the modeling mismatch and influence the performance of source localization of matched filed processor (MFP). The possibility of sequential tracking of source range and depth under the circumstances of time-evolving sound speed profiles is discussed. In this paper, we attempt to describe the time evolution characteristics of SSPs by analyzing the oceanographic dynamics which is the physical reason of the SSP’s change. A sequential approach considering the oceanographic dynamics model into the state-space equation is developed and applied. Synthetic simulations are carried out with experimental SSP data and acoustic pressure data from a vertical line array. Result suggests the sequential approach based on oceanographic dynamics model can improve the tracking capabilities.