In this paper, a novel infrared maritime small target detection method, called local dissimilarity measure with anti interference based on global graph clustering (LDMGGC), is proposed. The Wasserstein distance is introduced to calculate the dissimilarity of gray level distribution between a central region and its neighborhoods. These dissimilarities construct the feature of a region. With this feature, detection for recalling all suspected targets is achieved. As the maritime interferences among suspected targets are able to be clustered, relaxing mutual k nearest neighbor graph is introduced in global graph clustering for filtering interferences. With this method, real targets are detected and maritime interferences are filtered out. Experiments are conducted on three maritime datasets and a non-maritime dataset for comparison. On three datasets, the proposed method achieves the best Receiver Operating Characteristic curves and Area Under Curve (0.99529, 0.99945, 0.99573, and 0.9906) values, meaning that the proposed method has high detection probability and low false-alarm ratio. Target Hit Rate (98.04%, 97.96%, 100%, and 99.24%) and Intersection of Union (0.8170, 0.7542, 0.5824, 0.7707) on four datasets of the proposed method show it has a strong ability to suppress the interferences

The filterbank multicarrier (FBMC) waveform has better properties in spectral as compared to the Orthogonal Frequency Division Multiplexing (OFDM) and it exhibits good anti-interference performance. We mainly consider an important variety of the FBMC systems, namely FBMC based on offset orthogonal amplitude modulation (FBMC/OQAM). This letter presents an analysis of the residual Doppler effect in the multipath underwater acoustic (UWA) channel for the FBMC/OQAM system. Assuming the received signal has gone through coarse non-uniform Doppler compensation by resampling, we model the compensated signal with residual Doppler effect and multipath effect. Especially, we derive a simplified closed-form expression for the received FBMC signal after non-uniform Doppler compensation under the signal-to-interference ratio. The theoretical results and the simulations show better compliance in the UWA multipath channel. Furthermore, the results obtained demonstrate that FBMC has better BER performance than the cyclic prefix (CP) based OFDM in the case of residual Doppler effect under the UWA channel.

Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network’s focus on essential features, thereby improving the model’s generalization ability. To assess the model’s effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods.

Underwater acoustic (UWA) communication presents unique challenges due to the unpredictable and dynamic nature of acoustic channels, influenced by Doppler spread, low signal-to-noise ratios (SNRs), and the general need for complex channel characteristics, coupled with a scarcity of real-world data. Accurate orthogonal frequency division multiplexing (OFDM) channel estimation is pivotal for ensuring reliable data transmission in such challenging environments. In this study, we introduce the DenseNet estimator, which is specifically used for OFDM channel estimation in UWA communication. The use of dense connectivity within the DenseNet structure proves to be advantageous in capturing the intricacies of the complex and dynamic UWA channels. This architecture, showcasing robustness even when there's a limited number of pilots, sets it apart from conventional methods. The DenseNet estimator is trained on the WATERMARK data set, leveraging the richness of real-time varying channel impulse responses to provide the necessary diversity for accurate channel estimation. Uniquely, once trained, our DenseNet estimator operates without necessitating additional channel statistics like SNR, relying solely on the received signal as its primary input. This approach offers a simplified and more direct application in real-world scenarios. Our numerical results underscore the DenseNet estimator's efficacy: It consistently outperforms traditional methods such as least square, minimum mean square error, and fully connected neural network, recording improvements of up to 96.3%, 94.2%, and 40.0% in bit error rate. Performance assessments across various watermark underwater channels demonstrate the DenseNet estimator's adaptability and robustness in both stable and challenging environments.

Underwater acoustic communication plays an essential role in underwater wireless information exchange, and nodes with such an ability constitute an underwater acoustic sensor network (UASN). However, UASNs face many challenges, such as high bit error rate and high propagation delay, significantly reducing network performance. Because of the challenging nature of underwater environments, the power consumption and battery energy efficiency of sensor nodes need to be considered. Directional communication technology can conserve energy by restraining beam directivity and diminishing interference between nodes by enabling peer-to-peer communication with intended neighbors. However, the technology suffers from the problems of neighbor discovery, deafness effect, and hidden/exposed terminal. To address these challenges, we propose a full-duplex directional medium access control (MAC) framework based on the vector hydrophone and multimodal transducer in this paper, along with a benchmark MAC protocol, which enables adaptive power control to achieve spatial reuse and energy conservation. Simulation results show that the benchmark MAC protocol outperforms existing representative protocols in terms of throughput, end-to-end delay, and energy efficiency.

A novel network for enhancement of underwater optical images is proposed in this paper. It contains a Reinforcement Fusion Module for Haar wavelet (RFM-Haar) images. Fusion is performed to enrich available underlying information for better enhancement. As this network transform Haar Images into Fusion Images, it is called HIFI-Net. The experimental results based on Mean Square Error (0.1952, 0.1512 and 0.4683), Peak Signal to Noise Ratio (25.2241, 26.3342 and 21.4253) and Structure Similarity Index Measure (0.8265, 0.8819 and 0.8012) on three public datasets (EUVP, UFO-110 and UIEB) demonstrate that the proposed HIFI-Net performs better than some existing state-of-the-art methods.