Abstracts:This article presents orthogonal frequency division multiplexing (OFDM) that is based on discrete Hartley transform (DHT) as a modulation transform for underwater acoustic (UWA) communication instead of the conventional discrete Fourier transform (DFT). The proposed DHT-OFDM scheme has the advantages of reducing the pilot overheads required to track the UWA channel effects by 50%, increasing the robustness against the carrier frequency offset, and relaxing the system complexity. The receiver design of the proposed system is based on DFT instead of DHT to enable low-complexity estimation and equalization tasks and to overcome the issue of intercarrier coupling, which deteriorates up to half of the existed DHT-OFDM data rate. For more spectral efficiency harvesting, the proposed underwater DHT-OFDM has been extended to cooperate with the index modulation. Simulation and real experimental results have been conducted to demonstrate the outperformance of the proposed schemes against the benchmarks in terms of bit error rate, Doppler shift, spectral efficiency, and system complexity.

Abstracts:This article presents extensive field tests of a fast-mode operation of the JANUS acoustic communication standard where the signals occupy high-frequency bands of 38 kHz spanning from 96 to 134 kHz instead of the nominal frequency band of 4.1 kHz spanning from 9.44 to 13.6 kHz specified in the standard. The fixed 32-chip preamble and the 144-chip baseline JANUS packet utilize the frequency-hopped binary frequency shift keying with 13 frequency pairs as defined in the standard while the cargo packets use the single-carrier <italic>M</italic>-ary phase shift keying modulation with a center frequency of 115 kHz and a symbol rate of 23 ksps or up to 34.5 kb/s information data rate with high-order phase shift keying (PSK) and rate-1/2 forward error correction codes. The original JANUS receiver algorithm is modified to improve the frame/symbol synchronization for the fast mode and increase the decoding success rate of the baseline JANUS packet in difficult multipath channels. More than 10 experiments were conducted using a field-programmable-gate-array-based hardware platform consisting of a single transmit projector and a single receive hydrophone. The experiment results show that the JANUS fast mode worked well with both the original JANUS receiver algorithm and the modified receiver algorithm, yielding zero bit error in most of the baseline JANUS packets. The modified receiver algorithm is able to reduce 30&#x0025; of error packets to zero error in the experiment experiencing difficult multipath channels where the original JANUS receiver algorithm suffers from large bit error rates (BER). Meanwhile, the cargo packets utilize the linear minimum mean-square error turbo equalizer and achieve a BER around 10^&#x2212;3.

Abstracts:Sidescan sonar intensity encodes information about changes in the surface normal of the seabed. However, other factors such as seabed geometry as well as its material composition also affect the return intensity. One can model these intensity changes in a forward direction from the surface normals from a bathymetric map and physical properties to the measured intensity, or alternatively one can use an inverse model which starts from the intensities and models the surface normals. Here, we use an inverse model which leverages deep learning&#x0027;s ability to learn from data; a convolutional neural network is used to estimate the surface normal from the sidescan. Once this information is estimated, a bathymetric map can be reconstructed through an optimization framework that also includes altimeter readings to provide a sparse depth profile as a constraint. Implicit neural representation learning was recently proposed to represent the bathymetric map in such an optimization framework. In this article, we use a neural network to represent the map and optimize it under constraints of altimeter points and estimated surface normal from sidescan. By fusing multiple observations from different angles from several sidescan lines, the estimated results are improved through optimization. We demonstrate the efficiency and scalability of the approach by reconstructing a high-quality bathymetry using sidescan data from a large sidescan survey. We compare the proposed data-driven inverse model approach of modeling a sidescan with a forward Lambertian model. We assess the quality of each reconstruction by comparing it with data constructed from a multibeam sensor.

Abstracts:We consider an application where an unmanned underwater vehicle (UUV) equipped with an acoustic sensor seeks to estimate the location of surface ships using relative angle measurements to the ships. The estimation problem is challenging due to ships occasionally appearing and disappearing from the sensor&#x0027;s field of view. On occasion, poor geometry between the sensor and the ships, and port&#x2013;starboard ambiguity that is inherent in the sensor contribute to the challenges in the estimation problem. The latter challenge arises because the sensor cannot distinguish between sound sources on its port and starboard side (port&#x2013;starboard ambiguity). Therefore, every measurement is associated with two possible sound sources that map each relative angle to bearing projections on the port and starboard side of the UUV. We approach the problem of identifying the origin of sound sources (relative angle measurements) that are most likely to be of actual ships using Bayesian hypothesis testing. We propose an assignment method that uses the Bayes factor as the criteria to recursively associate measurements to the target that is most likely to be an actual ship and not a false target. The effectiveness of our approach is evaluated by comparing the performance of three multitarget tracking algorithms with and without our method. Performance is evaluated offline using sea-trial data captured by a UUV developed by the Naval Research Laboratory and Bluefin Robotics.

Abstracts:In this article, the effects of Morison drag force and the second-order hydrodynamics on a state-of-the-art floating system integrating a floating offshore wind turbine with a steel fish farming cage (FOWT-SFFC) are studied. To numerically solve the second-order hydrodynamic problem with ease, a simplified structure is adopted. Convergence study is carried out on the full difference-frequency quadratic transfer function (QTF). The middle-field method is recommended to be used in the calculation of QTF. For the aero-hydro-servo-elastic simulations of FOWT-SFFC, the time-domain solver OrcaFlex is used. The effects of the Morison drag force and second-order hydrodynamics on the dynamic responses under a variety of conditions are discussed. The comparison between the results with and without Morison drag force shows that Morison drag force has twofold effects on the low-frequency responses, making the excitation and damping level both larger. The results obtained by including first-order wave loads only and involving both first-order and second-order wave loads are also compared. The comparison reveals that the low-frequency contents of the dynamic responses, particularly heave and pitch, can be boosted when the second-order hydrodynamic forces are included. In contrast to the full QTF method, the known Newman&#x0027;s approximation leads to underprediction of the low-frequency responses of FOWT-SFFC.

Abstracts:The quality of underwater images is usually degraded due to the absorption and scattering effect of seawater, which leads to image distortion and reduces the accuracy and efficiency of subsequent vision tasks. To solve the above problems, an enhancement method for underwater images based on latent low-rank decomposition and image fusion is proposed in this article. First, a color correction method based on adaptive channel compensation is implemented to remove color distortion. Second, an improved Laplace sharpening method and gamma correction technology are applied to effectively improve the sharpness and contrast of the underwater image. Finally, a dual-image weighted image fusion based on latent low-rank representation is proposed to integrate the enhanced image. The experimental results show that this method can obtain better results than other traditional methods on both color reproduction and detail preservation. Furthermore, a set of application tests is executed to further demonstrate the effectiveness of the proposed method.

Abstracts:In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR&#x0027;s ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.

Abstracts:Safety-critical control is crucial but difficult in the applications of autonomous underwater vehicles (AUVs). This article proposes a novel hierarchical safety-critical framework for the control of AUVs consisting of waypoint-based optimal trajectory generation and tracking. The objective is to generate a smooth and feasible trajectory to conform to the dynamics of the AUV and then control the AUV to operate in a safe region. To this end, the offline trajectory generator and the online controller are designed. In the offline step, an improved minimum snap polynomial trajectory is generated as the tracking reference. Specifically, the trajectory is optimized by explicitly considering the practical constraints of the AUV&#x0027;s actuator, which alleviates the complexity of the controller design and computational burden compared with the real-time nonlinear optimization. In the online step, the time- and state-dependent high-order control barrier functions are incorporated into optimization through quadratic programming (QP) that modifies the backstepping-based nominal controller in a minimally invasive way. As a supplement and adjustment to the offline planning, the online step ensures that the system states are maintained in the safety set, thus ensuring obstacle avoidance. The online computational efficient QP structure guarantees convenience and scalability in practical implementation. Both the online static and dynamic obstacle avoidance simulations demonstrate the adherence to the safety constraints. Experimental results validate the effectiveness of the proposed method.

Abstracts:Considering different propulsion systems of unman- ned surface vehicles (USVs), it is necessary to design corresponding vessel motion controllers according to propulsion characteristics. This article devotes to solve the problem of trajectory tracking control for vector propulsion USV. By analyzing actuator distribution of vector propulsion, the vessel can be regarded as an incomplete underactuated system, and the control inputs adopt thruster speed and propulsion angle which are closer to reality. On this basis, the guidance trajectory and adaptive sliding mode controller based on virtual control point theory are proposed to realize trajectory tracking control of vector propulsion USV with system uncertainty and external disturbance. The designed guidance trajectory can guide the vessel to return to the desired trajectory when position error is large, and the system stability is illustrated by stability proof. Then in the design process of the controller, to mitigate chattering for sliding mode, a continuously derivable approximate saturation function is used instead of the signum function. Next, by zero-dynamics stability analysis, the position of the dynamic virtual control point is obtained with the relationship between the virtual control point and vessel speed. Finally, numerical simulations are carried out with two kinds of trajectory tracking scenarios to verify the correctness and feasibility of the proposed control strategy.

Abstracts:Unmanned surface vehicles (USVs) are of increasing importance to a growing number of sectors in the maritime industry, including offshore exploration, marine transportation, and defense operations. A major factor in the growth in use and deployment of USVs is the increased operational flexibility that is offered through use of optimized motion planners that generate optimized trajectories. Unlike path planning in terrestrial environments, planning in the maritime environment is more demanding as there is need to assure mitigating action is taken against the significant, random, and often unpredictable environmental influences from winds and ocean currents. With the focus on these necessary requirements as the main basis of motivation, this article proposes a novel motion planner, denoted as Gaussian process motion planning 2 star (GPMP2*), extending the application scope of the fundamental Gaussian-process-based motion planner, Gaussian process motion planning 2 (GPMP2), into complex maritime environments. An interpolation strategy based on Monte Carlo stochasticity has been innovatively added to GPMP2* to produce a new algorithm named GPMP2* with Monte Carlo stochasticity, which can increase the diversity of the paths generated. In parallel with algorithm design, a robotic operating system (ROS)-based fully-autonomous framework for an advanced USV, the Wave Adaptive Modular Vessel 20, has been proposed. The practicability of the proposed motion planner as well as the fully-autonomous framework has been functionally validated in a simulated inspection missions for an offshore wind farm in ROS.