Underwater source localization problems are complicated and challenging: a) the sound propagation speed is often unknown and the unpredictable ocean current might lead to the uncertainties of sensor parameters (i.e. position and velocity); b) the underwater acoustic signal travels much slower than the radio one in terrestrial environments, thus resulting into a significantly severe Doppler effect; c) energy-efficient techniques are urgently required and hence in favour of the design with a low computational complexity. Considering these issues, we propose a simple and efficient underwater source localization approach based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, which copes with unknown propagation speed and sensor parameter errors. The proposed method mitigates the impact of the Doppler effect for accurately inferring the source parameters (i.e. position and velocity). The Cramér-Rao lower bounds (CRLBs) for this kind of localization are derived and, moreover, the analytical study shows that our method can yield the performance that is very close to the CRLB, particularly under small noise. The numerical results not only confirm the above conclusions but also show that our method outperforms other competing approaches.

In underwater wireless sensor networks, time synchronization and localization are basic requirements in many applications. A joint synchronization and localization framework is expected to provide better accuracy. In this paper, we propose a unified framework to execute synchronization and localization simultaneously taking stratification effect into account. In this method, the stratification effect of underwater medium is modeled using a ray tracing approach. The maximum likelihood (ML) estimator is derived, which is shown to be highly nonlinear and nonconvex. Therefore, we employ the Gauss-Newton algorithm to solve the original nonconvex ML problem in an iterative manner. Furthermore, the Cramér-Rao lower bound for this problem is derived as a benchmark. Simulation results indicate that the proposed method outperforms the existing methods in both accuracy and energy efficiency.

Underwater acoustic signal denoising technology aims to overcome the challenge of recovering valuable ship target signals from noisy audios by suppressing underwater background noise. Traditional statistical-based denoising techniques are difficult to be applied effectively in complex underwater environments, especially in the case of extremely low signal-to-noise ratios (SNRs). To address these problems, we propose a noise-aware deep learning model with fullband-subband attention network (NAFSA-Net) for underwater acoustic signal denoising. NAFSA-Net adopts an encoder to extract the feature representation of the input audio. Subsequently, the noise subnet and the target subnet are designed to estimate the noise component and the target component simultaneously. Specifically, some stacked fullband-subband attention (FSA) blocks are deployed in each subnet to capture both global dependencies and fine-grained local dependencies of features. Furthermore, we introduce an interaction module to transmit auxiliary information from the noise subnet to the target subnet. Finally, we propose an improved weight SI-SNR loss function to optimize the training of our model. Experimental results show that our proposed NAFSA-Net substantially outperforms traditional methods and competitive DNN-based solutions in denoising underwater noisy signals with very low SNRs. More importantly, our proposals achieve equally excellent performance on both unseen datasets, which indicates that NAFSA-Net can be a more robust choice for real-world underwater acoustic denoising systems.

A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric function is proposed. Then, the proposed loss function is applied in a multi-scale residual convolutional neural network (named MR-CNN-A network) embedded with an attention mechanism for the recognition task. Firstly, a multi-scale convolution kernel is used to obtain multi-scale features. Then, an attention mechanism is used to fuse these multi-scale feature maps. Furthermore, a cosx-function-weighted cross-entropy loss function is used to deal with the class imbalance in underwater acoustic data. This function adjusts the loss ratio of each sample by adjusting the loss interval of every mini-batch based on cosx term to achieve a balanced total loss for each class. Two imbalanced underwater acoustic data sets, ShipsEar and autonomous underwater vehicle (self-collected data) are used to evaluate the proposed network. The experimental results show that the proposed network outperforms the support vector machine and a simple convolutional neural network. Compared with the other three loss functions, the proposed loss function achieves better stability and adaptability. The results strongly demonstrate the validity of the proposed loss function and the network.

The post-process is widely adopted in passive sonar to improve the performance of target detection by raising the bearing resolution and suppressing the background noise, whereas the conventional algorithms separately adopted on the sub-bands always perform poorly when detecting weak target. To solve this problem, this paper proposes a method of filtering scanning azimuths for uniform line array based on the features of sub-band peaks. The proposed method enhances the sub-band peaks through the energy selection, modified Eckart filter, and noise weighting, wherein the scanning azimuths are filtered based on the accumulated energy and count features to finally preserve those near the target azimuth, which improves the normalized azimuth spectrum for better detection effect. This approach is validated by both simulated and measured data, and provides an idea for detecting the weak target submerged by the strong background interferences.

The real-time position of an autonomous underwater vehicle (AUV) is always of great interest. With advances of technologies, underwater wireless sensor networks (UWSNs) become promising tools for AUV tracking. Due to the energy constraint of underwater nodes, energy saving is a key issue that affects all aspects of the design of a tracking scheme. In this paper, we propose a novel energy-efficient tracking scheme for an AUV to locate itself in time by UWSNs. We first design a tracking protocol considering the energy consumptions in both the AUV and sensor nodes (SNs). Particularly, the protocol is designed in two aspects: 1) the passive listening mechanism and duty-cycle strategy for the AUV and 2) the detection-based ranging packet transmission for SNs. Since the tracking protocol will inevitably affect the packet delivery between the AUV and SNs, we analyze the packet delivery success rate (PDSR) to shed light on the impact of system parameters on the tracking performance. To cope with non-linearity of the model and the intermittent observations mainly arisen from the effect of the tracking protocol, we adopt two extended versions of the original intermittent Kalman filter for tracking. They are intermittent extended Kalman filter and intermittent unscented Kalman filter. Simulation results demonstrate the effectiveness of the proposed tracking scheme, and reveal that the PDSR analysis provides a design guidance for parameter selection in system configuration.