The Internet of Underwater Things (IoUT) technology has been gaining importance in smart ocean investigation in recent years. The integrated system for underwater detection and communication (ISUDC) is considered a crucial technique for IoUT due to its potential to decrease equipment size and power consumption. The linear frequency modulation (LFM) signal has the characteristics of large time-bandwidth product and has great advantages in detection. However, it has high complexity and low robustness of communication in ISUDC. To address this issue, this article proposes a sector-modulated LFM (SM-LFM) signal for ISUDC. In terms of communication, it divides Fresnel domain into equal length sectors. Then, data is loaded by activating different sector. At the communication receiver, by establishing a relationship between the Dirac function in the Fresnel domain and the channel impulse response (CIR) in the time domain, data is demodulated by finding CIR’s main path without channel estimation or equalization. In terms of detection, SM-LFM has an improvement in the maximum unambiguous velocity by calculating relative radial velocity in the Fresnel domain instead of the frequency domain. Numerical simulations and lake experiments have confirmed that the SM-LFM signal can deliver superior robustness and easy-to-use advantages for underwater active detection and communication.

Joint communication and detection systems present distinct benefits in terms of spectral efficiency and cost-effectiveness for the Internet of Underwater Things (IoUT). Due to the unique characteristics of underwater acoustic channels, existing radar communication (RadCom) signals and detection methods have shortcomings when applied underwater: 1) Doppler ambiguity caused by signal features. We propose a novel incremental frequency interval acoustic frequency comb (IFI-AFC) signal which performs better autocorrelation performance in the Doppler dimension to overcome the Doppler ambiguity underwater. 2) Range and velocity constrain each other caused by the popular Range-Doppler process method. We propose the energy spectrum matching (ESM) algorithm which serves as an alternative to the Range-Doppler process in the underwater scenario, avoiding the constraint relationship between maximum measurable unambiguous range and velocity. The proposed sonar-communication (SonarCom) system based on IFI-AFC and ESM has excellent multitarget discrimination capability, making it more suitable for applications in SonarCom scenarios.

Inverted ultra-short baselines(iUSBL) systems provide low-cost and passive covert spatial sensing for autonomous underwater vehicles (AUV) networking, which is a prerequisite and advanced technology to support the Internet of Underwater Things (IoUT). In the iUSBL system, multiple AUVs can realize low-power autonomous positioning through passive reception of space-time information broadcast by a surface mothership beacon. However, existing iUSBL systems usually use different and independent communication and positioning signals due to the independent development of both technologies, which leads to wasted resources and inefficiencies. This paper presents a novel integrated acoustic frequency comb (IAFC) signal for communication and positioning integration of the iUSBL system. The IAFC signal with communication and positioning performances is designed by employing the spectral sparsity and orthogonal feature of the AFC. In particular, an AFC for positioning is created by employing the stable phase-locked characteristic. And an AFC for communication is generated via cyclic shift modulation. Compared to other integrated signals, IAFC achieves a comprehensive trade-off of positioning accuracy, spectral efficiency, robustness, and hardware compatibility by a fully-shared waveform design, avoids the corruption of positioning correlation by the randomness of communication coding. The experimental results proposed IAFC can implement positioning and communication simultaneously and enhance the spectral efficiency of the iUSBL system.

In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the nonorthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the underwater acoustic (UWA) channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this article, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse nonorthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the nonorthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to nonorthogonal subcarrier designing, with the idea that designing CM signal adaptive nonorthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and nonorthogonal modulation schemes in UWA scenarios.

This article considers underwater acoustic passive localization of a noncooperative broadband source in the presence of multipath propagation. A differential transmission-loss model-based passive localization method is proposed, in which 3-D localization can be implemented with only two hydrophones. Instead of spatial diversity, frequency diversity is exploited to achieve passive localization with a limited number of hydrophones and address the challenge of multipath propagation. Specifically, the received signals of both hydrophones are decomposed in the frequency domain, respectively, at first. Then, a set of differential transmission-loss model-based equations between the two hydrophones are established on multiple frequencies. Based on these equations, the passive localization is modeled as a multivariate optimization problem. Meanwhile, the nonline of sight (NLOS)-related parameters are also involved in the optimization problem. Therefore, accurate localization and NLOS mitigation can be accomplished simultaneously by solving the optimization problem. To simplify the multivariate optimization problem for a reliable solution, a cepstrum-autocorrelation-based multipath estimation algorithm is proposed, by which all the NLOS paths can be represented by an equivalent NLOS path. Consequently, the simplified optimization problem concerns only the desired coordinate of the source and a single NLOS-related parameter. Finally, a differential evolution algorithm is employed to solve the simplified optimization problem. Both simulation and lake trial results corroborate the effectiveness and the robustness of the proposed method.

In the Global Navigation Satellite System reflectometry (GNSS-R), the spectral analysis approach is widely used to derive sea level height from signal-to-noise ratio (SNR) data because of its simplicity and ease of implementation. However, it requires many corrections to improve accuracy such as data quality control, outlier removal, and SNR bias correction. Moreover, the correction methods are normally post-processing, which are not suitable for real-time estimation. In this paper, we propose a novel method using a combination of two neural networks, including the SNR Forward Network and Time-aware Long Short-Term Memory Model Anomaly Detection (T-LSTM-AD), to improve the spectral analysis approach to be more accurate and closer to real-time estimation. SNR Forward Network is the special neural network designed based on the SNR physical model with consideration of the surface roughness term. It learns SNR biases from historical SNR data and is applied to new SNR data for SNR biases correction without using other external information. T-LSTM-AD is a new approach for outlier detection and dynamic surface modeling based on the temporal dependency of data. The trained T-LSTM-AD is used to classify the outlier and correct the dynamic surface for the new estimated sea level. To verify the performance, 1-year data from GTGU is separated to the training and testing data. The results of testing data with 6.3 cm RMSE and 0.939 correlation coefficient show that the proposed method has good performance. Furthermore, when applied to 1-year data, the proposed method provides accurate results compared to the existing methods.

The accurate localization of nodes is one of the most basic tasks in underwater acoustic sensor networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this article, we propose a mobile-beacon-based iterative localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multihop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism first uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then, the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network's lifetime.

Received signal strength (RSS)-based ranging is a promising distance estimation approach in underwater acoustic sensor networks (UASNs). However, the multipath-rich underwater environment complicates acoustic propagations and derails the RSS-based ranging. To address the challenges, this article provides a novel ranging method, called channel state information (CSI)-based ranging for UASNs (CRUN). Instead of RSS, the measured CSI is modeled as a set of power-loss-based equations. Then, the ranging process under multipath scenarios is transformed as a multivariate optimization problem which involves parameters of all propagation paths. This optimization problem aims to simultaneously realize distance estimation and multipath mitigation. Noticing the large number of variables makes the solution numerically unstable, a threshold-window-based algorithm is proposed to simplify the multivariate optimization problem. In specific, the proposed algorithm extracts relative amplitude attenuations and relative time delays between the line-of-sight (LOS) path and each of the non-line-of-sight paths from CSI. The extracted parameters, being as equality constraints, simplify the multivariate optimization problem to a univariate optimization problem only concerning the desired LOS distance. Then, the simplified problem can be efficiently solved by the gradient descent algorithm. Statistical-channel-model-based simulations and lake experiments demonstrate that CRUN significantly improves the ranging accuracy and robustness compared with RSS-based approaches.

Research on underwater acoustic sensor networks has become a compelling field in recent years since resources on land are being depleted. Therefore, robust and efficient routing protocols are needed to ensure the reliability of message gathering and transmitting in underwater sensor networks. In the process of biological foraging in nature, creatures such as insects can find paths while adapting to dynamic environmental conditions through group cooperation, which provides a new perspective for research on routing protocols. In this study, the authors proposed an ant colony algorithm‐based routing protocol (ACAR). In ACAR, the pheromone with a novel physical meaning and concentration changing mechanism is utilised to guide ants (path establishing packets) to the sink node. In addition, node depth information is used to decrease redundancy in the underwater environment. The routing process can be summarised in three parts: pheromone list setup, routing decision and damaged path repair. Simulation results, carried out on an underwater simulator based on NS‐2, showed that ACAR outperforms other schemes with regards to network lifetime with a relatively better delivery ratio and latency in the proposed network scenarios.

Due to the characteristics of the underwater acoustic (UWA) channel, the process at the receiver is complicated to match the channel. To simplify receiver design and match UWA channel better, this study proposes a deep neural network‐based orthogonal frequency division multiplexing receiver for UWA communication. Different from existing receivers needing a neural network and several other processing parts, the proposed receiver only uses a single neural network to implement the whole signal processing. Moreover, it is a general receiver which is suitable for other modulation schemes. Simulation results show that the proposed receiver offers better bit error rate performance over traditional ones.

This study proposes a sparse non‐orthogonal frequency division multiplexing (S‐NOFDM) based on sparse representation to improve the spectral efficiency of orthogonal frequency division multiplexing (OFDM). The subcarriers of S‐NOFDM are generated from a subset of OFDM orthogonal subcarriers, which therefore requires less spectral resource. Each selected OFDM subcarrier is shifted into a group of overlapping subcarriers with different time delays. The modulated signal is produced by solving the sparse representation of the input signal under the generated set of subcarriers. The demodulation is simply a linear combination of generated subcarriers with the recovered modulated signal. Simulation results show that the proposed S‐NOFDM can achieve better bit error rate performance in an additive white Gaussian noise channel and a little worse than OFDM in a Rayleigh channel, with less frequency resources required.

Underwater images generally suffer from low contrast, serious noise and color distortion. The main challenges of underwater image enhancement are to preserve details in dark regions while avoiding oversaturetion in bright regions. This paper proposes a novel underwater image enhancement method based on image decomposition. By decomposing the high-frequency texture and noise into the texture layer, the transmission map is estimated from the noise-free structure layer to avoid the noise amplification problem in underwater image enhancement. Both the structure layer and texture layer are descattered with the estimated transmission map. After denoising by gradient residual minimizition, the texture layer is enhanced and added back into the structure layer to recover the final enhanced image. Experimental results verify that the proposed approach can recover the high-quality images with fine details and edges while improving contrast and color naturalness, especially for images taken in the high turbidity environment.

Moiré artifacts are generally caused by the interference between the overlap of the sensor's sampling grid and high-frequency (nearly) periodic textures, and heavily affect the image quality. However, it is difficult to effectively remove moiré artifacts from textured images as the structure of moiré patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoiréing method by signal decomposition and guided filtering. Given a textured image with moiré artifacts, we first remove moiré artifacts in the green (G) channel using the proposed low-rank and sparse matrix decomposition model. This model regularizes the texture layer by the low-rank prior in spatial domain and the moiré layer by sparse representation in frequency domain. An alternating direction method under the augmented Lagrangian multiplier framework is used to solve the matrix decomposition model. Then, since the red (R) and blue (B) channels are more heavily polluted by moiré artifacts than the G channel, we propose to remove moiré artifacts in its R and B channels via guided filtering by the obtained texture layer of the G channel. Experimental results demonstrate that our method outperforms the state-of-the-art methods for both synthetic and real images.