Orthogonal signal-division multiplexing (OSDM) is a promising modulation scheme that provides a generalized framework to unify orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. By partitioning each data block into vectors, it allows for a flexible configuration to trade off resource management flexibility with peak-to-average power ratio. In this paper, an OSDM system is proposed for underwater acoustic communications. The channel Doppler effect after front-end resampling is modeled as a common time-varying phase on all propagation paths. It leads to a special signal distortion structure in the OSDM system, namely, intervector interference, which is analogous to the intercarrier interference in the conventional OFDM system. To counteract the related performance degradation, the OSDM receiver performs iterative detection, integrating joint channel impulse response and phase estimation, equalization, and decoding in a loop. Meanwhile, to avoid inversion of large matrices in channel equalization, frequency-domain per-vector equalization is designed, which can significantly reduce the computational complexity. Furthermore, the performance of the proposed OSDM system is evaluated through both numerical simulations and a field experiment, and its reliability over underwater acoustic channels is confirmed.

Solving the nonconvex and non-differentiable objective function of traditional range-free node localization methods will result in low localization accuracy or high computational complexity. To this end, a novel iterative localization algorithm called CVX-DV-hop is proposed in this letter. It first performs matrix transformation to reformulate the original optimization problem into one with a convex function and nonconvex constraints. And then, the first-order Taylor expansions are employed to tighten the nonconvex constraints into linear inequality constraints. Finally, a successive convex approximation method is designed to iteratively solve the optimization problem. Simulation results show that the proposed algorithm has higher localization accuracy and lower computational burden than those of the particle swarm optimization algorithm.

Orthogonal signal-division multiplexing (OSDM) is a generalized modulation scheme to bridge the gap between orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. It allows significantly more flexibility in system design; however, over doubly-selective channels, it suffers from a special signal distortion structure, namely inter-vector interference, which is analogous to inter-carrier interference in conventional OFDM. To analyze its effect, in this paper, the complex exponential basis expansion model (CE-BEM) is used to approximate the doubly-selective channel. We show that the composite channel matrix of OSDM systems is cyclically block-banded in this case, and the blocks in its main band can be further diagonalized. By exploiting this unique matrix structure, low-complexity block and serial OSDM equalization algorithms are then proposed. These two equalization algorithms are based on block LDLH factorization and block iterative matrix inversion, respectively, both of which are implemented in a transformed domain to avoid direct inversion of large matrices. In addition, a CE-BEM channel estimation method is designed for OSDM systems, which uses frequency-shifted Chu sequences as pilots to ease the computation. Numerical simulations are finally provided to justify the validity of our channel equalization and estimation algorithms.

Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through techniques such as Siamese networks and prototypical networks. However, it also suffers from the issue of overfitting, which leads to catastrophic forgetting and performance degradation. Current underwater FSL methods primarily focus on mining similar information within sample pairs, ignoring the unique features of ship radiation noise. This study proposes a novel cross-domain contrastive learning-based few-shot (CDCF) method for UATR to alleviate overfitting issues. This approach leverages self-supervised training on both source and target domains to facilitate rapid adaptation to the target domain. Additionally, a base contrastive module is introduced. Positive and negative sample pairs are generated through data augmentation, and the similarity in the corresponding frequency bands of feature embedding is utilized to learn fine-grained features of ship radiation noise, thereby expanding the scope of knowledge in the source domain. We evaluate the performance of CDCF in diverse scenarios on ShipsEar and DeepShip datasets. The experimental results indicate that in cross-domain environments, the model achieves accuracy rates of 56.71%, 73.02%, and 76.93% for 1-shot, 3-shot, and 5-shot scenarios, respectively, outperforming other FSL methods. Moreover, the model demonstrates outstanding performance in noisy environments.

Although side-scan sonar can provide wide and high-resolution views of submarine terrain and objects, it suffers from severe interference due to complex environmental noise, variations in sonar configuration (such as frequency, beam pattern, etc.), and the small scale of targets, leading to a high misdetection rate. These challenges highlight the need for advanced detection models that can effectively address these limitations. Here, this paper introduces an enhanced YOLOv9(You Only Look Once v9) model named SOCA-YOLO, which integrates a Small Object focused Convolution module and an Attention mechanism to improve detection performance to tackle the challenges. The SOCA-YOLO framework first constructs a high-resolution SSS (sidescan sonar image) enhancement pipeline through image restoration techniques to extract fine-grained features of micro-scale targets. Subsequently, the SPDConv (Space-to-Depth Convolution) module is incorporated to optimize the feature extraction network, effectively preserving discriminative characteristics of small targets. Furthermore, the model integrates the standardized CBAM (Convolutional Block Attention Module) attention mechanism, enabling adaptive focus on salient regions of small targets in sonar images, thereby significantly improving detection robustness in complex underwater environments. Finally, the model is verified on a public side-scan sonar image dataset Cylinder2. Experiment results indicate that SOCA-YOLO achieves Precision and Recall at 71.8% and 72.7%, with an mAP50 of 74.3%. It outperforms the current state-of-the-art object detection method, YOLO11, as well as the original YOLOv9. Specifically, our model surpasses YOLO11 and YOLOv9 by 2.3% and 6.5% in terms of mAP50, respectively. Therefore, the SOCA-YOLO model provides a new and effective approach for small underwater object detection in side-scan sonar images.

In the Internet of Underwater Things, which is entirely composed of unmanned underwater vehicles (UUVs) without fixed beacons, synchronized underwater acoustic (UWA) localization systems are employed. These systems estimate the relative locations of the UUVs, enabling the formation of an optimal network topology. The localization accuracy is affected by the UUV motion, the localization and communication signal (LCS) bandwidth and duration, and the power of LCSs. In this paper, a reinforcement learning (RL)-based smart UUV localization scheme is proposed firstly. By optimizing the localization time windows and the signal weights, the RL-based localization scheme reduces the localization time delay and energy consumption while increasing the localization accuracy without fixed anchor nodes. Furthermore, a double deep Q-network (DDQN)-based hierarchical structure localization scheme is proposed to optimize the allocation of the sub-strategies for the slave UUVs, aiming to reduce the localization error and the energy consumption. Computational complexity and Cramer-Rao lower bounds (CRBs) are analyzed to evaluate the optimal localization performance. Simulation results show that the proposed schemes improve the localization accuracy, and reduce the time delay and the energy consumption compared with the benchmarks.