With the introduction of concepts such as ubiquitous mapping, mapping-related technologies are gradually applied in autonomous driving and target recognition. There are many problems in vision measurement and remote sensing, such as difficulty in automatic vehicle discrimination, high missing rates under multiple vehicle targets, and sensitivity to the external environment. This paper proposes an improved RES-YOLO detection algorithm to solve these problems and applies it to the automatic detection of vehicle targets. Specifically, this paper improves the detection effect of the traditional YOLO algorithm by selecting optimized feature networks and constructing adaptive loss functions. The BDD100K data set was used for training and verification. Additionally, the optimized YOLO deep learning vehicle detection model is obtained and compared with recent advanced target recognition algorithms. Experimental results show that the proposed algorithm can automatically identify multiple vehicle targets effectively and can significantly reduce missing and false rates, with the local optimal accuracy of up to 95% and the average accuracy above 86% under large data volume detection. The average accuracy of our algorithm is higher than all five other algorithms including the latest SSD and Faster-RCNN. In average accuracy, the RES-YOLO algorithm for small data volume and large data volume is 1.0% and 1.7% higher than the original YOLO. In addition, the training time is shortened by 7.3% compared with the original algorithm. The network is then tested with five types of local measured vehicle data sets and shows satisfactory recognition accuracy under different interference backgrounds. In short, the method in this paper can complete the task of vehicle target detection under different environmental interferences.

In marine mapping, the sound speed profile (SSP) is an essential parameter. The SSP reflects the characteristics of the ocean sound field’s vertical structure. Furthermore, analyzing the SSP has an important significance on the propagation of underwater sound signals. The empirical orthogonal function (EOF) can extract the SSP data’s main features. It is widely used in SSP fitting and other applications. In this paper, the studying area is 116°~117°E,18°~19°N in the South China Sea. Using the Argo data from 2010 to 2020, this paper first visually analyzes SSP, carries out the EOF’s analysis, and then elaborates the basic EOF principle. It confirms that the EOF has a good application value for the South China Sea, and the first sixth order function can accurately describe the sound speed profile. Then this paper analyzed the EOF’s temporal function. The study found that the third mode of temporal function strongly connects with the ocean’s climate condition. And the two severe fluctuations in 2010 and 2014 have a strong relationship with the typhoon passage. In other words, the degree of extreme fluctuations is positively correlated with typhoon intensity. In brief, understanding the SSP’s temporal pattern is beneficial for applying various engineering scenarios. It can improve the accuracy of sound speed correction of marine acoustic instruments, while the study of marine climate environment has an important role.

Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120° E, 6°–8° N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.

This letter presents the mechatronic design and implementation of a hybrid-driven disc-shaped autonomous underwater vehicle (HD-AUV). The hybrid-driven system integrates a buoyancy adjustment system and propeller thrusters, enabling the HD-AUV to achieve both high maneuverability motion and energy-efficient gliding. These capabilities correspond to two distinct motion modes: AUV mode and glider mode. In AUV mode, the HD-AUV leverages the rotational symmetry of its disc-shaped design to achieve four degree-of-freedom (4-DOF) motion control. This configuration, supported by four propeller thrusters, facilitates high-maneuverability actions, including fixed-point hovering and in-place turning. In glider mode, the integration of rotatable dorsal fins and horizontal propeller thrusters enables the HD-AUV to transition seamlessly between diving and ascending phases without the conventional mass-shifting mechanisms used in torpedo-type gliders. Numerical simulations are conducted to evaluate the steady glide performance of the HD-AUV, focusing on lift-to-drag ratios and hydrodynamic coefficients. Comprehensive pool experiments, encompassing multi-DOF maneuvers and gentle gliding, demonstrate the exceptional locomotion capabilities of the HD-AUV and validate the accuracy of the proposed dynamic model. These hybrid motion modes hold significant promise for underwater operations in complex seafloor environments.

In multi-sensor systems, time synchronization is particularly challenging for underwater integrated navigation systems incorporating acoustic positioning, where time delays can significantly degrade accuracy when measurement and fusion epochs are misaligned. This paper introduces a tightly coupled navigation framework that integrates a passive inverted ultra-short baseline (piUSBL) acoustic positioning system, a strapdown inertial navigation system (SINS), and a depth gauge under precise time synchronization. The framework fuses piUSBL azimuth and slant range with depth measurements, avoiding poor vertical-angle observability in planar arrays. By combining synchronized timing with acoustic signal processing, the proposed method transforms delay from an unobservable error into a measurable parameter, enabling explicit quantification of both acoustic propagation and system processing delays. Field experiments demonstrate that the proposed approach reduces position RMSE by 44.02% and maximum error by 40.79% compared to the uncompensated baseline, while achieving further RMSE reductions of 37.66% and 35.82% in horizontal directions relative to filter-based delay compensation. The results confirm that explicit delay measurement outperforms filter-based estimation, though instantaneous performance remains sensitive to acoustic signal quality, emphasizing the need for robust signal processing alongside accurate time synchronization in latency-sensitive multi-sensor systems.