In the marine environment, underwater targets are often affected by interference from other targets and environmental fluctuations, so traditional target tracking methods are difficult to use for tracking underwater targets stably and accurately. Among the traditional methods, the Kalman filtering method is widely used; however, it only has advantages in solving linear problems and it is difficult to use to realize effective tracking problems when the trajectory of the moving target is nonlinear. Aiming to solve this limitation, an LSTM–Kalman filtering method was proposed, which can efficiently solve the problem of overly large deviations in underwater target tracking. Using this method, we first studied the features of typical underwater targets and, according to these rules, constructed the corresponding target dataset. Second, we built a convolutional neural network (CNN) model to detect the target and determine the tracking value of the moving target. We used a long-term and short-term memory artificial neural network (LSTM-NN) to modify the Kalman filter to predict the azimuth and distance of the target and to update it iteratively. Then, we verified the new method using simulation tests and the measured data from an acoustic sea trial. The results showed that compared to the traditional Kalman filtering method, the relative error of the LSTM–Kalman filtering method was reduced by 60% in the simulation tests and 72.25% in the sea trial and that the estimation variance was only 4.79. These results indicate that the method that is proposed in this paper achieves good prediction results and a high prediction efficiency for underwater target tracking.

The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers’ attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. First, we use Kalman filtering to predict the azimuth and distance of the target, and then use the trained model to identify the azimuth-weighted time-frequency image to obtain the azimuth and label of the target and obtain the target distance by the time the target appears in the time-frequency image. Finally, we associate the data according to the target category, and update the target azimuth and distance information for this cycle. In this paper, two methods, KFAT and DCNN-KFAT, are simulated and tested, and the results are obtained for two cases of tracking weak signal-to-noise signals and tracking different targets with overlapping azimuths. The simulation results show that the DCNN-KFAT method can solve the problem that the KFAT method is difficult to track the target under the weak SNR and the problem that the target is easily lost when two different targets overlap in azimuth. It reduces the deviation range of the active tracking to within 200 m, which is 500~700 m less than the KFAT method.

The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas.

The fractional-order differential operator describes history dependence and global correlation. In this paper, we use this trait to describe the viscoelastic characteristics of the solid skeleton of a viscoelastic two-phasic porous material. Combining Kjartansson constant Q fractional order theory with the BISQ theory, a new BISQ model is proposed to simulate elastic wave propagation in a viscoelastic two-phasic porous material. The corresponding time-domain wave propagation equations are derived, and then the elastic waves are numerically simulated in different cases. The integer-order derivatives are discretised using higher-order staggered-grid finite differences, and the fractional-order time derivatives are discretised using short-time memory central differences. Numerical simulations and analysis of the wave field characterisation in different phase boundaries, different quality factor groups, and multilayered materials containing buried bodies are carried out. The simulation results show that it is feasible to combine the constant Q fractional-order derivative theory with the BISQ theory to simulate elastic waves in viscoelastic two-phasic porous materials. The combination can better describe the viscoelastic characteristics of the viscoelastic two-phasic porous materials, which is of great significance for further understanding the propagation mechanism of elastic waves in viscoelastic two-phasic porous materials and viscoelastic two-phasic porous materials containing buried bodies. This paper provides a theoretical forward simulation for fine inversion and reconstruction of layer information and buried body structure in viscoelastic two-phasic porous materials.

Conventional passive tracking methods for underwater acoustic targets in sonar engineering generate time azimuth histogram and use it as a basis for target azimuth and tracking. Passive underwater acoustic targets only have azimuth information on the time azimuth histogram, which is easy to be lost and disturbed by ocean noise. To improve the accuracy of passive tracking, we propose to adopt the processed multi-beam Low Frequency Analysis and Recording (LOFAR) as the dataset for passive tracking. In this paper, an improved LeNet-5 convolutional neural network model (CNN) model is used to identify targets, and a passive tracking method for underwater acoustic targets based on multi-beam LOFAR and deep learning is proposed, combined with Extended Kalman Filter (EKF) to improve the tracking accuracy. The performance of the method under realistic conditions is evaluated through simulation analysis and validation using data obtained from marine experiments.