Search Results (162)

The detection and localization of large underwater targets are important for maritime security, marine resource exploration, and underwater situational awareness, while the increasing acoustic stealth of underwater vehicles has limited conventional acoustic methods. This review provides a systematic overview of non-acoustic detection and localization technologies for large underwater targets, with emphasis on their relevance to unmanned aerial, surface, and underwater platforms. Wake-based detection, magnetic anomaly detection (MAD), and gravity anomaly detection (GAD) are reviewed as three representative non-acoustic routes. A bibliometric analysis is first conducted to summarize research trends, major contributors, and emerging hotspots. Wake-based methods are discussed in terms of wake signatures, modeling approaches, sensing platforms, and localization potential. MAD is analyzed from the perspectives of magnetic dipole modeling, target-based detection, noise-based detection, artificial intelligence (AI)-based detection, and magnetic localization. GAD is discussed with respect to physical feasibility, gravity-gradient target modeling, inversion methods, and engineering constraints. The review shows that wake-based methods are suitable for wide-area search and trajectory inference, MAD is relatively mature for short-range confirmation and localization, and GAD remains promising but less mature. Future research should focus on onboard sensors, platform stability, weak-signal extraction, background suppression, quantitative evaluation metrics, multi-source fusion, autonomous mission planning, and multi-platform collaboration. Full article

Long propagation delays, severe multipaths, and narrow bandwidths make feedback-based link adaptation impractical in UWA channels at kilometer ranges, so we replace the feedback step with a prediction step. The transmitter runs a two-layer coded OFDM link in which Polar codes handle bit errors, and Raptor fountain codes handle packet erasures, with the Raptor overhead (OH) as the only real-time knob. The OH is picked from a lookup table indexed by three quantities the receiver can estimate online: SNR, RMS delay spread, and Doppler frequency. Two CSI predictors feed that table: Temporal Multiple Sparse Bayesian Learning (TMSBL), which exploits delay-domain sparsity, and the Square-Root Unscented Kalman Filter (SRUKF), which tracks per-subcarrier variations. We evaluate the system in five channel environments (AWGN, Rayleigh, K-distribution, Bellhop ray-tracing, and synthetic proxies parameterized from the KAM11 and WATERMARK sea-trial statistics). Across the nine Bellhop scenarios, the adaptive link’s throughput gain over a fixed-OH ($\mathrm{OH}=1.5$) baseline at SNR $=4$ dB spans roughly $-4\%$ to $+30\%$, with the largest benefit in the marginal short-range cases (shallow 500 m, $+30\%$) where the fixed baseline is most over-provisioned and near-parity elsewhere. The scheme’s principal benefit is collapse prevention, tracking the Oracle within the safety margin and avoiding the throughput collapse the fixed baseline suffers at low SNRs. This effect is specific to the physically structured Bellhop channels; in the homogeneous Rayleigh and K-distribution channels, both schemes enter deep outage at very low SNRs, so it is not a universal guarantee. A 1000-trial high-resolution Rayleigh campaign sharpens the head-to-head between predictors: at SNR $=4$ dB, SRUKF + OH reaches PER $0.048$ (95% Wilson CI $[0.036, 0.063]$) and TMSBL + OH reaches $0.071$ ($[0.057, 0.089]$), and at SNR $=12$ dB, their throughputs ($0.748$ and $0.746$) are statistically indistinguishable from each other (95% Wilson halfwidth $\sim \pm$0.014) and lie close to the Oracle’s $0.768$ (within $\sim 0.02$). The two predictors therefore occupy overlapping operating regions once the safety margin is matched, and a sparsity-dependent tendency (TMSBL in sparse multipath, SRUKF in dense multipath) appears only in physically structured channels and only at the $n=100$ screening level, where it is not statistically resolved and would benefit from higher-trial confirmation. A finite-blocklength check confirms that CA-SCL-decoded Polar codes at $N=128$ stay within $0.5$ dB of the Polyanskiy normal approximation, which makes Polar a sensible inner code at UWA block lengths. Full article

The sound speed profile (SSP) is a core environmental parameter for underwater acoustic detection, navigation, communication, and other applications. However, its accurate acquisition is constrained by the sparsity of observational data and the ill-posed nature of inversion problems. This paper systematically reviews the research progress of SSP inversion under sparse observation constraints. The review traces the technical evolution from early physical models to current intelligent paradigms, classifies and compares mainstream inversion methods, presents typical application scenarios with quantitative case studies, provides a comparison of all kinds of SSP acquisition routes, and discusses critical challenges and future trends. The review reveals that current AI-driven methods achieve a practical accuracy of approximately 1–2 m/s but face bottlenecks in interpretability, cross-regional generalization, and extreme-condition robustness. Fusing physical constraints with multi-source sparse data (remote sensing, in-situ discrete measurements) emerges as the core direction for balancing inversion accuracy, efficiency, and cost. This paper provides a comprehensive reference for technical selection in marine acoustics, ocean observation, and underwater operations. Full article

Robust vessel detection from low-Signal-to-Noise Ratio (SNR) Distributed Acoustic Sensing (DAS) data benefits from exploiting spatial correlations among adjacent channels. The Cross-Channel Attention Fusion Network (CASFNet) is presented, utilizing a Cross-Channel Attention Fusion (CASF) mechanism to dynamically model dependencies among adjacent channels. This approach, based on a dual-component spectrogram representation, adaptively fuses local spatial context, enhancing signal coherence under low-SNR conditions. Experiments on real-world DAS data demonstrate superior accuracy and robustness compared to state-of-the-art methods, achieving a detection accuracy of 99.24% and an F1-score of 99.19%. Ablation results confirm the effectiveness of this spatial fusion strategy for vessel monitoring using submarine DAS data. Full article

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity. Full article

Forward-looking sonar (FLS) is an important sensing modality for autonomous underwater vehicles and other marine robotic systems operating in turbid, low-visibility, and acoustically cluttered environments. Reliable target detection in FLS imagery remains challenging because target echoes are often weak, compact targets can be obscured by background clutter, and embedded processors impose strict limits on model size, latency, and computation. To address these issues, this study presents a lightweight FLS sensing framework for embedded target detection in resource-constrained underwater systems. The framework combines a compact detection architecture, difficulty-aware supervision, and teacher–student knowledge transfer. Specifically, FPN-Mix is developed as a lightweight backbone with a Conv-Mix module to improve contextual aggregation under limited computational budgets. A target-aware dynamic weighting loss is introduced to increase the supervision weight of difficult acoustic samples associated with weak echoes, ambiguous boundaries, and clutter interference. A multi-level knowledge distillation strategy is then adopted to transfer feature-level and prediction-level knowledge from an enhanced teacher model to the compact student detector. Experiments on the public UATD benchmark and the independently collected Zhanjiang Bay No.1 field dataset show that the proposed method achieves a favorable balance between detection accuracy and efficiency and remains competitive in a real marine aquaculture environment. The proposed model contains only 2.83 M parameters and requires 6.68 GFLOPs. After ONNX export and TensorRT FP16 acceleration, the model reaches 72.23 frames per second (FPS) on an NVIDIA Jetson Orin NX platform, supporting its practical use in embedded FLS sensing systems. Full article

In this study, a flexible, lightweight high-frequency underwater transducer (FT) was designed and fabricated. To ensure the flexibility and reliability of the transducer, a flexible piezoelectric composite material with a same-side electrode configuration and a perforated flexible printed circuit (FPC) were designed. First, finite element simulation analysis was performed on the flexible piezoelectric composites to optimize the structural parameters. Next, using a cutting and infusion method combined with reflow soldering, the piezoelectric composites and FPC were integrated to form a flexible sensing element. Finally, a flexible packaging process for the underwater transducer was investigated, resulting in a flexible underwater transducer with a thickness of only 5.3 mm. The results of underwater electroacoustic comparison tests show that the transmission and reception performance of the FT differs by less than 0.5 dB from that of conventional rigid transducers. This demonstrates that the flexible underwater transducer developed in this study not only possesses the advantages of flexibility and can be conformally mounted on curved surfaces but also achieves acoustic performance comparable to that of traditional rigid transducers, thereby providing new insights for the lightweight development of underwater transducers. Full article

To address the demand for underwater acoustic detection with hydrostatic pressure resistance, this paper proposes a fiber-optic Fabry–Perot (F-P) underwater acoustic sensor based on micro-electromechanical system (MEMS) technology. According to the F-P interference principle, the diaphragm deforms under acoustic pressure, inducing variations in the F-P cavity length which modulate the interference spectrum and enable the measurement of underwater acoustic signals. A sensing diaphragm with a composite structure consisting of a central boss and a micro-hole array is designed, which improves the optical signal quality while reducing the influence of the pressure difference between the inner and outer surfaces of the diaphragm on sensor operation. MEMS fabrication, computer numerical control (CNC) machining, and laser fusion splicing technologies are employed to achieve batch fabrication of the sensing units and adhesive-free integration of the sensor. Experimental results show that the proposed sensor exhibits a flat frequency response within ±1.5 dB over the range of 1 kHz to 10 kHz, with an average signal-to-noise ratio (SNR) of 86.35 dB. The sensitivity reaches −181.79 dB re 1 rad/μPa at 10 kHz, with a maximum nonlinearity of 0.48% F.S., a repeatability error of 0.15% F.S. and a dynamic range of 100.83 dB. The proposed sensor features miniaturization, high consistency, hydrostatic pressure self-balancing capability, and immunity to electromagnetic interference, providing a solid foundation for hydrostatic-pressure-resistant underwater acoustic measurements in deep-sea environments. Full article

High-precision underwater perception and scene reconstruction are critical techniques for marine surveying and resource exploration. Multi-sensor data fusion is currently the dominant method in underwater sensing. In this paper, a new approach for underwater sensing based on an integration of a 3D rotating scanning imaging sonar, an RTK (Real-Time Kinematic), and an IMU (Inertial Measurement Unit) systems onboard an unmanned surface vehicle (USV) is raised. By employing multi-sensor data fusion and image correlation calibration, combined with multi-view acoustic image synthesis, the system achieves accurate reconstruction of both water column and seabed scenes. The new system offers high reconstruction accuracy, and provides a cost-effective solution for scene reconstruction with a low requirement of the precise motion control of the USV platform. High-precision seabed imaging results have been validated through lake bed imaging tests. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

Sonar is a fundamental tool for underwater target detection. However, conventional detection systems often suffer from poor sensor consistency and high fabrication costs. More critically, for low-frequency operation, the required array aperture becomes prohibitively large, limiting their deployment on small, mobile underwater platforms. To address the demand for compact, high-performance sensing solutions, this paper presents a miniaturized Micro-electromechanical Systems (MEMS) hydrophone array designed for underwater target detection. The array consists of six elements with a spacing of 0.25 m. Each element is approximately 22 mm in diameter and encapsulated in polyurethane via a casting and curing process. The core sensing element, a MEMS acoustic pressure hydrophone, exhibits a sensitivity of −177.2 ± 1.5 dB (re: 1 V/µPa) across the 20 Hz to 4 kHz frequency range and a noise resolution of approximately 59.5 dB (re: 1 µPa/√Hz) at 1 kHz. A key challenge in array-based detection is the phase mismatch among acquisition channels, which degrades algorithm performance. To mitigate this, we propose a phase self-correction method based on interleaved ADC acquisition control, enabling synchronous multi-channel sampling and effectively eliminating system-level phase errors. Furthermore, to overcome the inherent aperture limitations of conventional beamforming (CBF) applied to a miniaturized array, a differential beamforming (DBF) algorithm is adopted. This approach is less frequency-dependent and can approximate a frequency-invariant beam pattern, making it well-suited for miniaturized arrays. Simulation results confirm the theoretical validity of the DBF algorithm for the proposed MEMS hydrophone array. Sea trial data further demonstrate that this method achieves higher target detection accuracy compared to CBF techniques. Full article

Reliable underwater acoustic (UWA) communication is fundamental to marine sensing applications, including environmental monitoring, underwater sensor networks, and autonomous platforms, yet remains severely challenged by multipath propagation, Doppler effects, and limited bandwidth. This paper investigates a memory-based multicarrier modulation framework in which controlled phase continuity is introduced at the symbol-mapping stage to enhance robustness against channel-induced distortions. Unlike conventional memoryless multicarrier schemes, the proposed approach embeds intentional phase memory at the transmitter and exploits it at the receiver, improving reliability in highly dispersive underwater environments. A comprehensive bit-error-rate (BER) evaluation is conducted using extensive simulations over realistic shallow-water acoustic channel models. The analysis examines rational modulation indices, pulse-shaping filters, roll-off factors, transmitter–receiver separation distances, and receiver structures. Both matched-filter and zero-forcing receivers are considered to assess trade-offs between interference mitigation and noise amplification. Results demonstrate consistent and significant BER improvements compared with conventional memoryless multicarrier systems. A modulation index of 7/16 achieves the minimum BER with matched-filter detection, while 3/10 yields optimal performance with zero-forcing detection. The Dirichlet pulse provides the most robust performance across operating conditions. These findings establish phase-memory-aware multicarrier design as a practical strategy for reliable underwater sensing and communication systems. Full article

Multifrequency operation in micromachined ultrasonic transducers, enabled by targeted excitation of specific vibrational modes, has emerged as an attractive approach for achieving tunable performance and configurability, well-suited for advanced ultrasound imaging and therapeutic applications. This paper presents a dual-electrode rectangular piezoelectric micromachined ultrasonic transducer (PMUT) designed for efficient dual-frequency operation through mode-selective actuation. The proposed architecture employs segmented electrodes that are spatially aligned with the strain distributions of two distinct flexural modes, enabling selective excitation of Mode 1 (fundamental) and Mode 3 (higher order) through appropriate electrode actuation. Finite element simulations and impedance analysis were used to guide the electrode configuration and validate the mode-selective behavior. The dual-mode PMUT was fabricated alongside a conventional single-electrode PMUT using identical membrane dimensions and material stack for direct comparison. Comprehensive electrical and underwater acoustic characterization confirmed that the conventional PMUT is limited to single-frequency operation at the fundamental resonance. In contrast, the proposed design achieved a substantial improvement in higher-order performance, with a threefold increase in acoustic pressure at Mode 3 compared to the conventional device. These results demonstrate that mode-aligned electrode segmentation enables efficient dual-mode operation without added fabrication complexity, making the design highly suitable for multifrequency ultrasonic applications such as biomedical imaging and sensing. Full article

Underwater vehicles (UVs) are pivotal for ocean exploration, yet their effectiveness is fundamentally constrained by acoustic performance in noisy and dynamic seas. Self-noise, non-stationary interference, and extreme conditions not only degrade sensing, navigation, and stealth but also cascade into losses in propulsion efficiency, actuation reliability, and control precision. This review provides a system-performance-oriented synthesis of advances across four key areas: bioinspired and intelligent noise reduction materials/structures, active noise control and adaptive signal processing, noise-robust navigation and collaborative localization, and deep learning-enhanced acoustic perception. Key findings indicate that bioinspired surfaces reduce flow noise by ≈5 dB, adaptive filtering improves $\mathrm{SNR}$ by up to 20 dB, and distributed robust filtering ensures multi-AUV consistency under uncertainty. These developments collectively establish acoustic performance not as a parallel metric, but as a fundamental enabler and critical bottleneck for the integrated propulsion-actuation-control stack of next-generation UVs. Consequently, this review outlines viable pathways toward high-performance acoustic–mechanical integration. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article