Real-time, accurate and reliable monitoring of marine environmental information plays a crucial role in marine disaster warning and prediction, disaster prevention and reduction, marine resource development, and ensuring marine safety. In recent years, with the continuous development and upgrading of global navigation satellite systems (GNSS), the detection of atmospheric and marine environmental information based on GNSS navigation signals has become a new method and a hot research topic in the marine environmental monitoring technology. This method has been widely applied to domains such as marine meteorological monitoring and numerical forecasting. This article systematically reviews the current research status of the GNSS technology in marine environmental monitoring, including effective wave height, wind speed, rainfall intensity, water vapor and tide level monitoring. Furthermore, this paper systematically summarizes new technologies and methods and looks forward to provide reference for the future research in related fields.

Underwater biological object detection is crucial for aquaculture, endangered species protection,and ecological environment monitoring. This study comprehensively analyzes the applications of various deep learning methods in underwater biological object detection. The commonly used underwater biological object detection datasets are introduced. The state-of-the-art underwater biological object detection methods are classified, analyzed, and summarized by two stages and one stage. The actual applications of various detection methods are thoroughly described, and the advantages and disadvantages of their optimization strategies are analyzed and summarized. Future works in the field of underwater biological object detection based on deep learning are presented. This study provides a reference basis for researchers in the field of underwater biological object detection.

Water dynamics analysis was conducted on a compact and portable autonomous underwater vehicle(AUV) with side-scan sonar and amodified AUV with streamlined side-scan sonar. The analysis focused on examining the drag forces experienced by both AUVs at different speeds. The results demonstrated that the streamlined side-scan sonar effectively reduced pressure and viscous drag forces, resulting in an overall drag reduction of 15.4% at a normal speed of 3 knots, with a 9% reduction in viscous drag and an 18% reduction in pressure drag.At a high speed of 6 knots, the overall drag was reduced by 10.1%, with a 4.2% reduction in viscous drag and a 12% reduction in pressure drag. These findings demonstrate that optimizing the streamlined design of the AUV with side-scan sonar can effectively enhance the dynamic performance of the AUV, reduce its drag force, and improve its efficiency and performance.

This study proposes an efficient wave sensor fault diagnosis method based on wavelet packet decomposition, dimension reduction, and k-nearest neighbor algorithm(KNN) classification network to address the difficulty of wave sensor fault diagnosis, unidentifiable fault types, and time-consuming diagnosis. First, the standard deviation of the original signal is normalized. The normalized data is then subjected to a three-layer wavelet packet decomposition. The extracted feature vectors represent normalized data from the eight bands on layer 3. The second step involves using the t-distributed stochastic neighbor embedding (t-SNE) algorithm to reduce the dimension of the feature data. Finally, the dimension-reduced feature data is input into the KNN classification network for fault classification and detection. Experimental results show that the proposed method can improve the accuracy and diagnosis speed of the wave sensor fault diagnosis, with a diagnosis accuracy of up to 93.55% for normal and six faulty conditions.