Underwater acoustic communication (UAC) is one of the core technologies for the Internet of Underwater Things (IoUT). Channel estimation (CE) is crucial for achieving reliable UAC performance. However, existing underwater acoustic CE methods involve a trade-off between computational overhead and estimation accuracy. To relieve this issue, this paper proposes an efficient and high-precision CE algorithm. Based on compressed sensing theory, the algorithm first obtains an on-grid delay estimate via orthogonal matching pursuit (OMP). Subsequently, we optimize the delay estimation result beyond the grid constraint using an anadromic gradient descent (AGD) algorithm combined with the Armijo backtracking line search approach. The simulation results demonstrate that the proposed AGD-Armijo algorithm achieves comparable CE performance to existing same type of off-grid CE algorithms, while improving the iterative convergence rate by approximately 52.7%. Furthermore, to address the pseudo-path estimation problem caused by off-grid delay errors during the on-grid estimation stage, we propose a two-stage optimization off-grid channel estimation (TSO-OGCE) method. The TSO-OGCE can effectively suppress pseudo-paths and achieve reliable estimation performance by introducing a single-path off-grid optimization step. We have demonstrated the feasibility and effectiveness of the above methods through field experiments at Songhua Lake.

Integrated sensing and communication (ISAC) technology is crucial for next-generation underwater networks. However, covering multiple users and targets and balancing sensing and communication performance in complex under-water acoustic (UWA) environments remains challenging. This paper proposes an interleaved orthogonal frequency division multiplexing-based MIMO UWA-ISAC system, which employs a horizontal array to simultaneously transmit adaptive waveforms for downlink multi-user communication and omnidirectional target sensing. A multi-objective optimization framework is formulated to maximize the product of communication rate and range (PRR) while ensuring sensing performance and peak-to-average power ratio (PAPR) constraints. To solve this mixed-integer nonconvex problem, a two-dimensional grouped random search algorithm is developed, efficiently exploring subcarrier interleaved patterns and resource allocation schemes. Numerical simulations under real-world UWA channels demonstrate the designed system’s superiority and effectiveness: our algorithm achieves 90% faster convergence than conventional exhaustive search with only a marginal 0.5 kbps•km PRR degradation. Furthermore, the proposed resource allocation scheme maintains robustness beyond the baseline allocation schemes under stringent PRR and PAPR constraints.

Underwater acoustic sensor networks (UASNs) play a key role in ocean resource exploration and complex underwater tasks. However, the open acoustic channel makes them vulnerable to malicious node attacks. Therefore, accurately identifying attack nodes in harsh channels and adapting to their mobility presents a significant challenge. To address these issues, we adopt a meandering ocean current mobility model to describe node movement and construct a hidden Markov model (HMM) along with link transmission loss to characterize the unstable underwater acoustic channel. By monitoring the forwarding behavior of neighboring nodes and combining HMM state inference, we propose a trust model based on a subjective logic framework with dynamic topology updates to detect malicious nodes. It considers variable weights to assess improper node behavior and dynamically updates trustworthiness based on both historical trust and arrival strategies of new and old nodes. Simulation results indicate that the proposed method effectively identifies malicious nodes with attack intensities exceeding 0.38, and for intensities above 0.6, it achieves over 90% identification accuracy and adapts well to dynamic environmental mobility.

Integrated sensing and communication (ISAC) technology is a promising contender for the future Internet of Underwater Things (IoUT). However, the complexity of underwater acoustic (UWA) channels and the randomness of ISAC signals may pose challenges to underwater communication and sensing. To address this issue, this paper investigates a novel communication-assisted bi-static sensing scheme capable of facilitating underwater multi-node collaboration using orthogonal frequency division multiplexing (OFDM), which refers to as UWA-OFDM-ISAC. Moreover, two efficient receivers are designed based on compressed sensing to enhance communication and sensing performance. In this paper, we first portray the UWA-OFDM-ISAC system model and emphasize that the Doppler and symbols estimated at the communication side are beneficial in enhancing the bi-static sensing performance. To estimate doubly spread UWA channels, an orthogonal matching pursuit-based interference cancellation channel estimation method is developed, which decouples Doppler and delay in OFDM signals and significantly reduces the parameter search dimension. Furthermore, we propose an enhanced detection algorithm based on matching pursuit, which can exploit sparse multipath information of echoes to improve target detection performance under doubly spread channels. The detection probability is improved by more than 30% at the 10−2bit error rate level compared with the energy detector. Finally, simulation results illustrate the effectiveness of the proposed UWA-OFDM-ISAC and demonstrate that the designed receivers have significant advantages relative to various existing algorithms.

Integrated sensing and communication (ISAC) technology is envisioned as a pivotal component for the next-generation communication networks. Similarly, underwater acoustic ISAC (UWA-ISAC) holds promising prospects for enhancing future UWA sensor networks due to its efficient communication and sensing capabilities. However, the design of UWA-ISAC waveforms and receivers suitable for multi-user scenarios faces formidable challenges, primarily arising from the complex UWA channel and multi-access interference (MAI). In this paper, we propose a UWA-ISAC waveform design scheme based on generalized sinusoidal frequency modulation (GSFM). The proposed waveform provides satisfactory communication and sensing performance and exhibits excellent orthogonality. Furthermore, we design a MAI suppression receiver, leveraging successive interference cancellation based on two-factor compensation and turbo equalization to improve interference suppression capabilities and enhance communication performance. Simulation results validate that the proposed UWA-ISAC waveform has an approximate thumbtack ambiguity function and comparable cross-correlation properties with GSFM under the defined parameters. Moreover, the designed receiver with low training sequence overhead is robust against Doppler, and can iteratively improve the MAI suppression performance.

Unexploded Ordnance (UXO) buried in the seafloor poses a serious threat to the environment and human safety. Removing UXOs in the ocean presents a tricky challenge due to the greater difficulty in controlling the damage caused by explosions compared to those on land. Therefore, this study designs a method for detecting seafloor-buried UXO using the transient electromagnetic (TEM) approach and proposes a new forward model for UXO characteristic responses in the ocean by modeling the marine environments as a two-layer medium. We first numerically solve the time-harmonic equations of the primary magnetic field using Sommerfeld integrals and Hankel transforms. Then, we derive the characteristic responses of UXO in seawater based on the three-dimensional magnetic dipole model and the TEM method. Finally, we simulate the characteristic responses of six typical UXOs and two interfering targets, comparing them with those in free space and analyzing the effects of type, measured distance, and buried attitude on identification. The results show that the characteristic responses in seawater delay 2-12 ms with target size compared to free space. The errors of the characteristic response measurement depend not only on the measured distance but also on the buried attitude of the target. The errors reach the maximum when the target is vertical, the minimum when horizontal at the same distance, and disappear when the measured distance is longer than twice the target size. These findings establish a crucial foundation for accurately identifying the type, burial state, and location of UXO during marine demining.

Joint detection and communication (JDC) systems can implement both functionalities simultaneously using the same hardware and software resources. This feature proves advantageous in reducing the size and power consumption of underwater vehicles. This paper develops a JDC system based on multi-input multi-output sonar by using orthogonal linear frequency modulation (OLFM) waveforms. Here, the proposed OLFM-based JDC system (OLFM-JDC) considers the detection functionality as the primary task. Therefore, OLFM-JDC exploits the mainlobe of transmit beam to detect targets and the sidelobes to communicate with the remote receivers. To enhance the information embedding capacity, the waveform diversity and the combination of index and phase modulations are utilized. Particularly, we propose a low-complexity two-step decoder to simplify the information decoding. Furthermore, the two-step decoder effectively utilizes multipath information to improve the performance of index decoding, and it can even outperform the maximum likelihood scheme when assessed within a simulated South China Sea acoustic channel. The numerical results demonstrate that OLFM-JDC achieves higher data rates and lower error rates compared to the JDC systems that only utilize phase modulation. Additionally, the simultaneous transmission of multiple waveforms facilitates target detection by utilizing the generalized high-resolution range profile synthesis technique. Performance analysis indicates that OLFM-JDC exhibits similar resolution performance to systems implementing a wideband waveform.