This paper presents a dynamic adaptive forgetting sparse kernel recursive least squares (DAFS-KRLS) model for predicting time-varying underwater acoustic (UWA) channels and applies it to an adaptive orthogonal frequency-division multiple access (OFDMA) system. By introducing an offline-online joint training mechanism, the DAFS-KRLS model adapts to the time-varying nature of UWA channels, thereby improving the realtime performance and stability of channel prediction. Simulation and sea trial data are used to validate the DAFS-KRLS model, with comparisons to traditional recursive least squares (RLS), approximate linear dependency based KRLS (ALD-KRLS), and convolutional neural networks (CNN) combined with long shortterm memory (LSTM) models (CNN-LSTM). Experimental results show that the DAFS-KRLS model achieves robust performance even with a smaller data volume, outperforming other models in accuracy and stability.

This paper tackles the highly challenging problem of automatic berthing for autonomous surface vessels (ASVs), encompassing trajectory planning, trajectory tracking, and collision avoidance. Firstly, a novel A* algorithm integrated with a quasi-uniform B-spline and quadratic interpolation method (A*QB) is proposed for generating a smooth trajectory from the initial position to the berth, utilizing an offline-generated scaled map. Secondly, the optimal nonlinear model predictive control (NMPC)-based trajectory-tracking framework is established, incorporating the model’s uncertainty, the input saturation, and environmental disturbances, based on a 3-DOF model of a ship. Finally, considering the collision risks during port berthing, a COLREGs-based collision avoidance method is investigated. Consequently, a novel trajectory-tracking and COLREGs-based collision avoidance (TTCCA) scheme is proposed, ensuring that the ASV navigates along the desired trajectory, safely avoids both static and dynamic obstacles, and successfully reaches the berth. To validate the TTCCA approach, numerical simulations are conducted across four scenarios with comparisons to existing methods. The experimental results demonstrate the effectiveness and superiority of the proposed scheme.

The filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) waveform demonstrates strong anti-interference capabilities due to its sub-carrier-level filtering. As a result, it is increasingly being adopted for underwater acoustic communication (UWAC) in challenging transmission environments. This paper presents a closed-form analytical expression for the bit error rate (BER) performance of FBMC-OQAM in shallow water, long-distance horizontal communication scenarios. The expression considers the residual Doppler frequency offset, multipath propagation, and ambient noise characteristic of underwater acoustic channels, accounting for the limitations of traditional non-uniform Doppler coarse compensation methods. Theoretical numerical results and Monte Carlo simulation results show that under any number of symbols, the derived closed-form expression consistently exhibits high computational accuracy, when facing the impact of Doppler spread or compression at any scale.

The advancement of unmanned platforms is driving the miniaturization and cost reduction of the multi-beam echosounder (MBES). In the process of MBES array calibration, the mutual coupling significantly impacts the performance of parameter estimation. We propose a correction method to mitigate the mutual coupling effects in the calibration of MBES acoustic array. Initially, a near-field focused beamforming model is established to assess the influence of mutual coupling. Subsequently, the covariance matrix in the frequency domain is constructed to enhance algorithm efficiency and simplify solution procedures. This construction eliminates the need for a low-pass filtering step after heterodyning through extracting peak values near zero frequency in the signal frequency domain. Meanwhile, the Toeplitz property is leveraged to render the estimation results independent of the mutual coupling matrix. Finally, the mutual coupling coefficients and the direction of arrival (DOA) are joint-estimated and the Cramér–Rao bound is derived. The presented method effectively addresses the engineering challenge of MBES mutual coupling calibration. Additionally, the performance of the proposed method is verified through the measured data in simulation and tank experiments.

To address the challenges posed by large propagation loss and severe multipath delay spread, while aiming to improve communication rates in long-range underwater acoustic (UWA) communication, this paper introduces a novel approach: multi-beam (MB) Orthogonal Frequency Division Multiplexing (OFDM) based on deconvolved conventional beamforming (dCv). The proposed method establishes a signal processing framework for UWA channel receivers, wherein received signals corresponding to each angle beam are isolated and concentrated using the dCv technique. Subsequently, the separated signals from each angle undergo maximal-ratio combining (MRC) channel equalization followed by demodulation. Compared to conventional single-beam (SB) processing methods, the proposed approach capitalizes on multipath diversity gain achieved by combining MB outputs originating from various arrival angles. Moreover, employing dCv processing demonstrates superior performance compared to popularly employed beamforming techniques, especially when dealing with paths arriving from closely aligned angles. Additionally, the array beamforming utilized significantly enhances the signal-to-noise ratio (SNR) of each beam output, mitigating the elevated error rates in channel estimation associated with combining low SNR signals received from individual elements. Simulation results utilizing BELLHOP and experimental data from the South China Sea showcase notably improved bit error rate (BER) performance for the proposed method compared to SB equalization, MB-MRC equalization based on conventional beamforming (CBF), minimum variance distortionless response (MVDR), and worst-case performance optimization (WCPO), as well as standard MRC equalization techniques. The proposed receiver achieves error-free decoding results at a communication distance of 80 km with a data rate of 247 bps.

In recent years, multibeam sonar has become the most effective and sensitive tool for the detection and quantitation of underwater gas leakage and its rise through the water column. Motivated by recent research, this paper presents an efficient method for the detection and quantitation of gas leakage based on a 300-kHz multibeam sonar. In the proposed gas leakage detection method based on multibeam sonar water column images, not only the backscattering strength of the gas bubbles but also the size and aspect ratio of a gas plume are used to isolate interference objects. This paper also presents a volume-scattering strength optimization model to estimate the gas flux. The bubble size distribution, volume, and flux of gas leaks are determined by matching the theoretical and measured values of the volume-scattering strength of the gas bubbles. The efficiency and effectiveness of the proposed method have been verified by a case study at the artificial gas leakage site in the northern South China Sea. The results show that the leaking gas flux is approximately between 29.39 L/min and 56.43 L/min under a bubble radius ranging from 1 mm to 12 mm. The estimated results are in good agreement with the recorded data (32–67 L/min) for gas leaks generated by an air compressor. The experimental results demonstrate that the proposed method can achieve effective and accurate detection and quantitation of gas leakages.

Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.

针对水中气体目标检测存在的水下环境复杂、探测目标多等问题,本文提出一种基于多波束测深声呐的水中气体综合检测方法.针对水中气体目标静态和动态特征,综合利用了一维波束输出、二维图像输出和三维声呐图像序列信息,在波束域上利用自适应阈值多次检测算法检测水体目标;在图像域中,采用数学形态学提取目标轮廓;针对三维声呐图像序列,利用尺度不变特征流估计气体目标的运动特征.水池和外场实验数据表明:自适应阈值提高了多次检测算法的鲁棒性;基于稠密匹配和图像金字塔理论的SIFT Flow算法能够从多波束测深声呐图像序列中估计气体目标运动特征,为水中气体目标的分布规模定量评估奠定基础.

In recent years, most multibeam echo sounders (MBESs) have been able to collect water column image (WCI) data while performing seabed topography measurements, providing effective data sources for gas-leakage detection. However, there can be systematic (e.g., sidelobe interference) or natural disturbances in the images, which may introduce challenges for automatic detection of gas leaks. In this paper, we design two data-processing schemes to estimate motion velocities based on the Farneback optical flow principle according to types of WCIs, including time-angle and depth-across track images. Moreover, by combining the estimated motion velocities with the amplitudes of the image pixels, several decision thresholds are used to eliminate interferences, such as the seabed, non-gas backscatters in the water column, etc. To verify the effectiveness of the proposed method, we simulated the scenarios of pipeline leakage in a pool and the Songhua Lake, Jilin Province, China, and used a HT300 PA MBES (it was developed by Harbin Engineering University and its operating frequency is 300 kHz) to collect acoustic data in static and dynamic conditions. The results show that the proposed method can automatically detect underwater leaking gases, and both data-processing schemes have similar detection performance.

Nested arrays exhibit higher spatial resolution (SR) and enhanced degrees-of-freedom (DOFs) with fewer sensors, and they have been utilized for direction-of-arrival (DOA) estimation of both far-field (FF) and near-field (NF) sources. In this study, an improved symmetric nested array configuration with a given number of sensors was developed, which achieved increased consecutive and unique lags and thus resolved more targets than the actual number of inherent array sensors. In particular, the analytical expressions of the number of consecutive lags, the number of unique lags, and the virtual array aperture were derived for quantitative evaluation and comparison, as well as the corresponding array composition parameters of the optimal array geometry were obtained. In the mixed sources localization scheme, a special cumulant matrix was constructed to eliminate the range parameter in the NF steer vectors by exploiting the symmetric feature. Both subspace and sparse reconstruction techniques were exploited in order to directly obtain the DOAs of both FF and NF sources. With the estimated DOAs and the covariance matrix of the array output, the NF sources could be identified, and the corresponding range parameters could also be obtained by a one-dimension (1-D) spectrum search scheme. Numerous simulation results demonstrated that the proposed array showed a remarkable performance in terms of estimation accuracy, SR capacity, and numerous DOFs compared to state-of-the-art symmetric nested arrays. (C) 2020 Elsevier Inc. All rights reserved.

Recently, coprime array has been a popular research field in the application of direction-of-arrival estimation. Compared with uniform linear array, coprime array can be used to expand array aperture with fewer sensors, and it also has a nice direction-of-arrival estimation performance. According to coprime property, the direction of arrival can be obtained by intersecting the candidate estimation sets from several subarrays. However, when the directions of multiple sources meet a particular relation, the unambiguous phase cannot be unwrapped through the coprime array. In this article, a multi-coprime array is proposed to address the problem, utilizing about half number of array elements and reducing hardware complexity compared with uniform linear array. Then, a low-complexity beamforming interferometry algorithm via multi-coprime array is proposed to reduce computational complexity. Numerical results, including simulation and actual data processing, are provided to indicate that the processing on multi-coprime array can successfully resolve phase ambiguity. Specially, when signal-to-noise ratio exceeds about −4 dB and the element number of subarray in multi-coprime array exceeds 15 for the array geometry in this article, the proposed method achieves close estimation performance with fewer sensors compared with uniform linear array.

Terrain-aided navigation is a promising approach to submerged position updates for autonomous underwater vehicles by matching terrain measurements against an underwater reference map. With an accurate prediction of tidal depth bias, a two-dimensional point mass filter, only estimating the horizontal position, has been proven to be effective for terrain-aided navigation. However, the tidal depth bias is unpredictable or predicts in many cases, which will result in the rapid performance degradation if a two-dimensional point mass filter is still used. To address this, a marginalized point mass filter in three dimensions is presented to concurrently estimate and compensate the tidal depth bias in this paper. In the method, the tidal depth bias is extended as a state variable and estimated using the Kalman filter, whereas the horizontal position state is still estimated by the original two-dimensional point mass filter. With the multibeam sonar, simulation experiments in a real underwater digital map demonstrate that the proposed method is able to accurately estimate the tidal depth bias and to obtain the robust navigation solution in suitable terrain.

In recent years, there has been an increasing requirement for methods of detecting bubbles released from the seabed into the water column, such as leaks from undersea gas pipelines and seeps from carbon capture and storage facilities. Considering the peculiarity of the layout of submarine gas pipelines and that of the ocean environment around them, we construct an underwater mobile platform equipped with autonomous underwater vehicles carrying multi-beam sonars and various types of other sensors. Analogous to optical flow, this paper describes a scale-invariant feature transform (SIFT) flow algorithm, which includes both the detection of key points and the computation of local descriptors. This SIFT flow algorithm estimates the motion characteristics of gas leaks and quantifies the flux of gas. Finally, the validity of this method is verified in pool and sea experimental research.

To guarantee good sparsity reconstruction quality, a suitable dictionary should be as orthogonal as possible. Also, to reduce the coherence of a dictionary, a sensing matrix optimisation model should be formulated. In this model, a high-dimension O(N) problem has to be considered because the sparse representation base is overcompleted, thus leading to a heavy computational load. This study proposes an efficient gradient-based method to address the dictionary optimisation problem of the time-varying arrays, whose elements relatively move in an arbitrary but known way. The dictionary optimisation models associated with different array geometries are formulated with distinct structure characteristic, and the Toeplitz and circulant properties are incorporated into the corresponding models to reduce the complexity by dimension reduction from O(N) to O(2) and O(1), respectively. An alternating minimisation approach for sensing matrix design (SMD) is derived by the gradient descent method, and an adaptive stepsize selection method is derived to further reduce complexity. Numerous simulations are conducted, and simulation results demonstrate that the presented methods have lower computational complexity but similar sparse recovery performance as the normalisation-constrained SMD method. Also, the effectiveness of the proposed structured optimisation framework to design a sensing matrix of a time-varying array is verified.

随着海底油气管道的铺设规模越来越大,为防止管道油气泄漏而导致人员伤亡和财产损失,海底管道的日常巡检尤为重要.提出一种基于多波束点云的海底管道检测与三维重建算法,利用高频多波束声纳对水下管道进行成像,对声纳图像采用经典边缘检测算法检测管道边缘,得到相应的点云数据,将点云数据拼接成一组完整的空间点云集合,对点云集合进行三维重建.通过水池试验结果证明,提出的算法流程能够有效地实现水下管道的检测与三维重建,具有较好的工程应用前景.

A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson⁻Lucy (R⁻L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it possible for real-time high-resolution beamforming in a multibeam sonar system. This paper applies the new fast deconvolved beamforming algorithm to a high-frequency multibeam sonar system to obtain a high bearing resolution and low side lobe. In the sounding mode, it restrains the tunnel effect and makes the topographic survey more accurate. In the 2D acoustic image mode, it can obtain clear images, more details, and can better distinguish two close targets. Detailed implementation methods of the fast deconvolved beamforming are given, its computational complexity is analyzed, and its performance is evaluated with simulated and real data.

Terrain aided navigation (TAN) is a promising approach to bound accumulated errors inherent to inertial navigation system by comparing terrain measurement with a reference map. Due to the non-linear nature of terrain, particle filters (PFs) are extensively studied for TAN because of its strong capability of dealing with non-linear problems. So far, most existing PFs for TAN manually select a fixed number of sampling particles during the entire filtering process. However, it can be highly inefficient, since the probability distribution of the state often varies drastically over time. To improve the efficiency, the Fox's adaptive PF based on Kullback–Leibler distance (KLD) is introduced for TAN, referred to as the normal KLD-PF here. In the normal KLD-PF, the number of sampling particles is adjusted online by KLD-sampling according to the size of state space. However, the normal KLD-PF has a fixed bin size, which easily causes the number of particles to surge at the early filtering stage. Thus, an improved KLD-PF with a variable bin size is proposed through limiting the total number of particles below certain value. Using a multi-beam sonar, simulation experiments with real underwater reference map demonstrate the efficiency of the proposed method.

As the increase of the use of seabed gas pipeline, it is urgently necessary to detect the leakage of gas pipeline. In this paper, an imaging and detection algorithm of subaqueous bubbles based on multi-beam sonar is presented. The core of the imaging algorithm is MVDR( minimun variance distortionless response) beamforming al-gorithm, the water body objective is imaged by sonar method after calculating position information and backscatter-ing intensity information. For the sonar image, the edge detection algorithm based on mathematic morphology is used to detect the edge of target and judge if the bubble exists or not. The results of the pool experiment show that the algorithm of this paper is effective for the imaging and detection of subaqueous bubble, it has an excellent engi-neering application prospect.

New developments in multibeam technology now permit MBES to collect and record acoustic data not only from the strongest return (normally the seabed), but also echo returns from the complete travel paths of the acoustic pulse through the water column. This now allows they are established as standard tools for the remote detection of targets in the water column, such as gas bubbles leaking from pipeline. In this study, a multibeam sonar operating at 300kHz is used to detect the gas leakage of pipeline based on acoustic backscatter imagery. Some behavioural traits of the leakage gas bubbles have been discussed, such as shape, distribution pattern and contour centroid characteristics. Firstly, an adaptive beamforming algorithm is applied to sonar imaging for suppressing background noise and side lobe interference. And then these features are extracted by mathematic morphological processing of image sequences. Finally, a tank test with different leakage scales caused by leakage pressures, amounts and sizes has verified the validity and stability of the characteristics of gas bubbles. The proposed method is feasible to make a qualitative assessment for AUV pipeline detection surveys.