With the extensive application of sensor technology in scientific ocean research, ocean resource exploration, underwater engineering construction, and other fields, underwater target positioning technology has become an important support for the ocean field. This paper proposes a robust positioning algorithm that combines the disadvantages of distributed estimation and particle swarm optimization, which can solve the large localization error problem caused by uncertainties in underwater acoustic communication and sampling processes. Considering the presence of ranging anomalies and sampling packet loss in underwater acoustic measurements, a weighted coupling filling method is used to correct the outliers in an underwater acoustic ranging signal. Based on the mapping model from the element array to the underwater acoustic responder, an unconstrained optimization algorithm for one-time localization estimation of underwater acoustic targets was established. Based on the one-time localization estimation results of underwater acoustic targets, an improved multi-particle swarm optimization estimation based on interactive search is proposed, which improves the accuracy of underwater target localization. The numerical results show that the positioning accuracy of the proposed algorithm can be effectively enhanced in cases of distance measurement errors and azimuth measurement errors. Compared with the positioning error before optimization, the positioning error can be reduced after optimization. Additionally, the experiment was carried out in the underwater environment of Hangzhou Qiandao Lake, which verified the positioning performance of the proposed algorithm.

The filter-bank multi-carrier (FBMC) with offset quadrature amplitude modulation (OQAM) is considered a promising waveform for 5G due to its favorable frequency-time localization and efficient spectrum utilization. However, the specific overlapping structure and multi-carrier nature of FBMC-OQAM waveforms result in an exceptionally high peak-to-average power ratio (PAPR). To address this issue, this letter introduces a linear companding transform scheme tailored for FBMC-OQAM waveforms to reduce the PAPR. Since, the unique modulation technique leads to a high probability of extreme amplitude occurrences in the FBMC-OQAM waveform, with the amplitude’s probability density function closely resembling a Laplace distribution rather than a Gaussian distribution. We derive the companding gain expression for the FBMC-OQAM signal and establish a parameter division criterion suitable for the FBMC-OQAM waveform under the assumption of a Laplace distribution. The simulation results reveal that while upholding optimal bit error rate performance, the proposed scheme provides at least 58% and 20% performance advantages in PAPR reduction capability compared to the Gaussian distribution-based linear nonsymmetrical transform and linear companding transform schemes, respectively.

Ship detection holds a pivotal position in intelligent maritime transportation systems, ensuring the stable development of maritime engineering. However, the complex marine environment and practical deployment requirements pose numerous challenges to ship detection tasks. To address these limitations, this paper proposes a robust and real-time ship object detection method based on enhanced CNN (RRSD). Firstly, an efficient feature extraction module (EFEM) is proposed, which introduces a cross-stage stacked enhanced multi-head self-attention to reduce the computational complexity of the model while enhancing the network’s feature extraction capability. Next, a feature fusion network (FFM) is introduced to enhance the feature representation capability of small objects by effectively aggregating features of different scales in the backbone. To focus on the attention regions in the image, designing a lightweight enhanced attention module (EAM) to improve the model’s inference speed and classification ability. Additionally, by utilizing data augmentation techniques without any additional cost, the overall performance of the model is intuitively and efficiently improved. The proposed method is extensively experimented on a self-built ship detection dataset (SDD2024) through qualitative and quantitative experiments. The experimental results indicate that RRSD achieves a detection performance of 88.01mAP50 (mean average precision at an intersection over union of 50) and 7.8ms, significantly outperforming object detection models of the same scale, and achieving a good trade-off between detection accuracy and speed. Furthermore, robustness experiments and embedded deployment tests demonstrate its good detection accuracy, fast speed, and satisfactory robustness in ship detection tasks, which basically meet the practical requirements of deployment and implementation.

The popularity of next-generation networks has brought ubiquitous location-based demands. This article proposes a node deployment-aided intelligent optimization estimation (NDIOE) for target positioning in terms of node deployment, wireless ranging, calibration errors, and the algorithm itself. Since the optimized deployment of anchor nodes is the basis of high-accuracy positioning, we derive the characteristic matrix closely related to the upper bound of positioning error and map the signal domain to the location domain for a preliminary result solved by unconstrained optimization estimation. On this basis, the preliminary locations as the initial search values can be further refined for more accurate locations with the use of improved local particle swarm optimization in a narrowed searching area, whose adaptive fitness function, inertia weight factor, and mutation operation are designed to avoid falling into the local optimal. Aside from that, considering the high computational cost of solving extreme values through node traversal, we theoretically derive the theoretical minimum expression of any point based on Cramer–Rao lower bound (CRLB) as a benchmark, whose theoretical minimum value is $0.2^{2}$ in the fully connected network. Compared with the existing related algorithms, the proposed NDIOE algorithm is effectively validated under simulation and experimental platforms, as well as in complex environments with obstacles.

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.