Pub Date : 2024-04-09DOI: 10.1109/JOE.2024.3360532
Mohammed Elamassie
This article explores an underwater communication system using visible light, featuring multiple parallel relay paths and several decode-and-forward relays per path. In traditional multihop systems, the failure of a single relay can result in the collapse of the entire system, rendering single-path multihop systems unreliable. Therefore, the adoption of parallel paths becomes important to enhance system robustness. In pursuit of reducing hardware complexity, the primary goal is to select one path from these parallel options. However, the challenge lies in choosing the best path, given various factors such as noise affecting channel coefficient estimation and the impact of erroneous feedback channels. In light of these challenges, our investigation delves into a comprehensive evaluation of the $lmathrm{{th}}$ best path selection in underwater environments. We consider both weak and moderate/strong turbulence conditions, with “weak” and “moderate/strong” turbulence conditions modeled by lognormal (LN) and gamma–gamma (GG) distributions, respectively. Closed-form expressions for the exact, approximate, and asymptotic probability of an outage are derived for both distributions when the $lmathrm{{th}}$ best path is chosen for transmission. Additionally, we explore the incremental diversity order for LN turbulence channels and investigate the diversity order for GG turbulence channels. This comprehensive approach enables a more robust evaluation of the system's performance under various underwater conditions.
{"title":"Path Selection in Parallel Multihop UVLC Systems Over Turbulence Channels","authors":"Mohammed Elamassie","doi":"10.1109/JOE.2024.3360532","DOIUrl":"10.1109/JOE.2024.3360532","url":null,"abstract":"This article explores an underwater communication system using visible light, featuring multiple parallel relay paths and several decode-and-forward relays per path. In traditional multihop systems, the failure of a single relay can result in the collapse of the entire system, rendering single-path multihop systems unreliable. Therefore, the adoption of parallel paths becomes important to enhance system robustness. In pursuit of reducing hardware complexity, the primary goal is to select one path from these parallel options. However, the challenge lies in choosing the best path, given various factors such as noise affecting channel coefficient estimation and the impact of erroneous feedback channels. In light of these challenges, our investigation delves into a comprehensive evaluation of the \u0000<inline-formula><tex-math>$lmathrm{{th}}$</tex-math></inline-formula>\u0000 best path selection in underwater environments. We consider both weak and moderate/strong turbulence conditions, with “weak” and “moderate/strong” turbulence conditions modeled by lognormal (LN) and gamma–gamma (GG) distributions, respectively. Closed-form expressions for the exact, approximate, and asymptotic probability of an outage are derived for both distributions when the \u0000<inline-formula><tex-math>$lmathrm{{th}}$</tex-math></inline-formula>\u0000 best path is chosen for transmission. Additionally, we explore the incremental diversity order for LN turbulence channels and investigate the diversity order for GG turbulence channels. This comprehensive approach enables a more robust evaluation of the system's performance under various underwater conditions.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"1116-1126"},"PeriodicalIF":3.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.1109/JOE.2024.3369663
Fan Wu;Haiyang Yao;Haiyan Wang
Ship-radiated noise recognition is an essential but complex task in the construction of marine information systems and marine scientific research. Ambient noise, unstable frequency shifts, and irregular multipath interference make it complicated to recognize ship-radiated noise accurately. Existing recognition methods exhibit constrained proficiency in the identification of the motion states of ships, thus leading to disappointing application performance. To effectively recognize the ship movement with less computation, this work proposes the time-frequency Swin-Transformer (TFST) network. A hierarchical self-attention module is presented to extract multilayer time-frequency features so that the TFST network could learn moving targets' features in TF representations of the noise radiated by moving targets. A scale-difference simplified architecture is designed to reduce network complexity. Experiments reveal that the TFST network outperforms the state-of-the-art convolutional neural networks (CNNs) and Transformers on two underwater acoustic data sets. Moreover, the TFST network achieves at least 1.3 times improvement compared to five state-of-the-art methods on both average accuracy (OA) and kappa coefficient in three motion status recognition experiments.
{"title":"Recognizing the State of Motion by Ship-Radiated Noise Using Time-Frequency Swin-Transformer","authors":"Fan Wu;Haiyang Yao;Haiyan Wang","doi":"10.1109/JOE.2024.3369663","DOIUrl":"10.1109/JOE.2024.3369663","url":null,"abstract":"Ship-radiated noise recognition is an essential but complex task in the construction of marine information systems and marine scientific research. Ambient noise, unstable frequency shifts, and irregular multipath interference make it complicated to recognize ship-radiated noise accurately. Existing recognition methods exhibit constrained proficiency in the identification of the motion states of ships, thus leading to disappointing application performance. To effectively recognize the ship movement with less computation, this work proposes the time-frequency Swin-Transformer (TFST) network. A hierarchical self-attention module is presented to extract multilayer time-frequency features so that the TFST network could learn moving targets' features in TF representations of the noise radiated by moving targets. A scale-difference simplified architecture is designed to reduce network complexity. Experiments reveal that the TFST network outperforms the state-of-the-art convolutional neural networks (CNNs) and Transformers on two underwater acoustic data sets. Moreover, the TFST network achieves at least 1.3 times improvement compared to five state-of-the-art methods on both average accuracy (OA) and kappa coefficient in three motion status recognition experiments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"667-678"},"PeriodicalIF":3.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.1109/JOE.2024.3369861
Benjamin Thomas;Ciaran Sanford;Alan J. Hunter
The ability to simulate realistic coherent data sets for synthetic aperture imaging systems is crucial for the design, development, and evaluation of sensors and their signal processing pipelines, machine learning algorithms, and autonomy systems. In the case of synthetic aperture sonar (SAS), collecting experimental data is expensive, and it is rarely possible to obtain ground truth of the sensor's path, the speed of sound in the medium, and the geometry of the imaged scene. Simulating sonar echo data allows signal processing algorithms to be tested with known ground truth, enabling rapid and inexpensive development and evaluation of signal processing algorithms. The de facto standard for simulating conventional high-frequency (i.e., $> {text{100}}$ kHz) SAS echo data from an arbitrary sensor, path, and scene is to use a point- or facet-based diffraction model. A crucial part of this process is acoustic occlusion modeling. This article describes a SAS simulation pipeline and compares implementations of two occlusion methods: 1) a ray-tracing method and 2) a newer approximate method based on finding the convex hull of a transformed point cloud. The full capability of the simulation pipeline is demonstrated using an example scene based on a high-resolution 3-D model of the SS Thistlegorm shipwreck, which was obtained using photogrammetry. The 3-D model spans a volume of $text{220}times text{130}times text{25},text{ m}$ and is comprised of over 30 million facets that are decomposed into a cloud of almost 1 billion points. The convex-hull occlusion model was found to result in simulated SAS imagery that is qualitatively indistinguishable from the ray-tracing approach and quantitatively very similar, demonstrating that the use of this alternative method has potential to improve speed while retaining high fidelity of simulation. The convex-hull approach was found to be up to four times faster in a fair speed comparison with serial and parallel central processing unit (CPU) implementations for both the methods, with the largest performance increase for wide-beam systems. The fastest occlusion modeling algorithm was found to be graphics processing unit (GPU)-accelerated ray tracing over the majority of scene scales tested, which was found to be up to two times faster than the parallel CPU convex-hull implementation. Although GPU implementations of convex-hull algorithms are not currently readily available, the future development of GPU-accelerated convex-hull finding could make the new approach much more viable. However, in the meantime, ray tracing is still preferable, since it has higher accuracy and can leverage the existing implementations for high-performance computing architectures for better performance.
模拟合成孔径成像系统真实相干数据集的能力对于设计、开发和评估传感器及其信号处理管道、机器学习算法和自主系统至关重要。就合成孔径声纳（SAS）而言，收集实验数据的成本很高，而且很少有可能获得传感器路径、介质声速和成像场景几何形状的地面实况。通过模拟声纳回波数据，可以利用已知的地面实况对信号处理算法进行测试，从而快速、低成本地开发和评估信号处理算法。模拟来自任意传感器、路径和场景的常规高频（即 $> {text{100}}$ kHz）SAS 回波数据的事实标准是使用基于点或面的衍射模型。这一过程的关键部分是声学闭塞建模。本文介绍了一个 SAS 仿真管道，并比较了两种闭塞方法的实现情况：1）光线跟踪方法；2）基于寻找转换后点云凸壳的较新近似方法。模拟管道的全部功能通过一个基于 SS Thistlegorm 沉船高分辨率三维模型的示例场景进行了演示，该模型是通过摄影测量获得的。该三维模型的体积为 $text{220}timestext{130}timestext{25}text{m}$，由超过 3000 万个面组成，这些面被分解成近 10 亿个点的云。研究发现，凸壳遮挡模型产生的模拟 SAS 图像在质量上与光线跟踪方法没有区别，在数量上也非常相似，这表明使用这种替代方法有可能在保持高仿真度的同时提高速度。通过与串行和并行中央处理器（CPU）实现的两种方法进行公平的速度比较，发现凸壳方法的速度最多可提高四倍，其中宽光束系统的性能提高最大。在测试的大多数场景尺度中，最快的闭塞建模算法是图形处理器（GPU）加速光线跟踪，其速度比并行 CPU 凸壳实现快两倍。虽然 GPU 实现的凸壳算法目前还不容易获得，但 GPU 加速凸壳搜索的未来发展可能会使新方法更加可行。不过，在此期间，光线追踪仍然更可取，因为它具有更高的精度，并且可以利用高性能计算架构的现有实现来获得更好的性能。
{"title":"Occlusion Modeling for Coherent Echo Data Simulation: A Comparison Between Ray-Tracing and Convex-Hull Occlusion Methods","authors":"Benjamin Thomas;Ciaran Sanford;Alan J. Hunter","doi":"10.1109/JOE.2024.3369861","DOIUrl":"10.1109/JOE.2024.3369861","url":null,"abstract":"The ability to simulate realistic coherent data sets for synthetic aperture imaging systems is crucial for the design, development, and evaluation of sensors and their signal processing pipelines, machine learning algorithms, and autonomy systems. In the case of synthetic aperture sonar (SAS), collecting experimental data is expensive, and it is rarely possible to obtain ground truth of the sensor's path, the speed of sound in the medium, and the geometry of the imaged scene. Simulating sonar echo data allows signal processing algorithms to be tested with known ground truth, enabling rapid and inexpensive development and evaluation of signal processing algorithms. The de facto standard for simulating conventional high-frequency (i.e., \u0000<inline-formula><tex-math>$> {text{100}}$</tex-math></inline-formula>\u0000 kHz) SAS echo data from an arbitrary sensor, path, and scene is to use a point- or facet-based diffraction model. A crucial part of this process is acoustic occlusion modeling. This article describes a SAS simulation pipeline and compares implementations of two occlusion methods: 1) a ray-tracing method and 2) a newer approximate method based on finding the convex hull of a transformed point cloud. The full capability of the simulation pipeline is demonstrated using an example scene based on a high-resolution 3-D model of the SS Thistlegorm shipwreck, which was obtained using photogrammetry. The 3-D model spans a volume of \u0000<inline-formula><tex-math>$text{220}times text{130}times text{25},text{ m}$</tex-math></inline-formula>\u0000 and is comprised of over 30 million facets that are decomposed into a cloud of almost 1 billion points. The convex-hull occlusion model was found to result in simulated SAS imagery that is qualitatively indistinguishable from the ray-tracing approach and quantitatively very similar, demonstrating that the use of this alternative method has potential to improve speed while retaining high fidelity of simulation. The convex-hull approach was found to be up to four times faster in a fair speed comparison with serial and parallel central processing unit (CPU) implementations for both the methods, with the largest performance increase for wide-beam systems. The fastest occlusion modeling algorithm was found to be graphics processing unit (GPU)-accelerated ray tracing over the majority of scene scales tested, which was found to be up to two times faster than the parallel CPU convex-hull implementation. Although GPU implementations of convex-hull algorithms are not currently readily available, the future development of GPU-accelerated convex-hull finding could make the new approach much more viable. However, in the meantime, ray tracing is still preferable, since it has higher accuracy and can leverage the existing implementations for high-performance computing architectures for better performance.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"944-962"},"PeriodicalIF":3.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The remote passive detection of vessels in the oceans is a significant activity for improving port security and the security of coastal and offshore operations. There still needs to be an efficient approach to achieve weak ship signal detection with nonparametric and noninformation priors. This study proposes a new multiscale correlation network construction method to effectively distinguish the ship from the ambient noise, which should be promising. Meanwhile, to effectively characterize the constructed network, we render definite the topological network matrix positive definite, then introduce the matrix into the Riemann space to measure the distance between the topology matrix of the noise and the signal by using the geodesic distance. Those methods are demonstrated by simulation and applied to actual recorded data. Compared with the existing network construction and characterization methods, the results show that multiscale correlation network and geodesic distance (GD) methods can distinguish nonlinear time series from noise more effectively.
{"title":"Multiscale Correlation Network and Geodesic Distance for Remote Passive Ship Detection in Marine Environment","authors":"Hongwei Zhang;Haiyan Wang;Yongsheng Yan;Xiaohong Shen;Qinzheng Zhang","doi":"10.1109/JOE.2024.3383924","DOIUrl":"10.1109/JOE.2024.3383924","url":null,"abstract":"The remote passive detection of vessels in the oceans is a significant activity for improving port security and the security of coastal and offshore operations. There still needs to be an efficient approach to achieve weak ship signal detection with nonparametric and noninformation priors. This study proposes a new multiscale correlation network construction method to effectively distinguish the ship from the ambient noise, which should be promising. Meanwhile, to effectively characterize the constructed network, we render definite the topological network matrix positive definite, then introduce the matrix into the Riemann space to measure the distance between the topology matrix of the noise and the signal by using the geodesic distance. Those methods are demonstrated by simulation and applied to actual recorded data. Compared with the existing network construction and characterization methods, the results show that multiscale correlation network and geodesic distance (GD) methods can distinguish nonlinear time series from noise more effectively.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"992-1008"},"PeriodicalIF":3.8,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the exploitation of natural gas hydrates intensifies, there is a growing imperative to enhance the monitoring of extraction and storage areas. However, existing monitoring methods, such as seismic detection and seabed drilling technology, exhibit inherent limitations. These shortcomings primarily stem from challenges associated with conducting prolonged, in situ monitoring and the constrained scope of exploration. Addressing these shortcomings necessitates the development of innovative exploration methods or devices. This article introduces Stratloong, a novel underwater exploration robot designed specifically for drilling in seabed stratum. Comprising a drill bit, front and rear support units, and a propulsion unit, Stratloong emulates the peristaltic motion of an earthworm to achieve efficient drilling. In this research, kinematic and dynamic models of the robot are formulated, and a task-based control method based on inverse kinematic control is presented. In addition, a generic motion control framework is proposed to realize the drilling motion. Straight drilling tests are conducted in prepared seabed clay under different static settlement times to assess Stratloong's performance. Data collected include rotational speed, displacement, and axial force during motion. The robot maintained over 90% motion efficiency in the prepared seabed clay. Furthermore, outdoor tests confirmed the robot's ability to drill into soil without external thrust. The robot advanced 2230 mm with 89% motion efficiency. The comprehensive evaluation of Stratloong's drilling capabilities, conducted through a series of laboratory and field tests, yields valuable data and experiences for its potential application in seabed strata exploration.
{"title":"Development and Motion Mechanism of a Novel Underwater Exploration Robot for Stratum Drilling","authors":"Peihao Zhang;Jiawang Chen;Haisheng Xia;Zhijun Li;Xingshuang Lin;Peng Zhou","doi":"10.1109/JOE.2024.3383883","DOIUrl":"10.1109/JOE.2024.3383883","url":null,"abstract":"As the exploitation of natural gas hydrates intensifies, there is a growing imperative to enhance the monitoring of extraction and storage areas. However, existing monitoring methods, such as seismic detection and seabed drilling technology, exhibit inherent limitations. These shortcomings primarily stem from challenges associated with conducting prolonged, in situ monitoring and the constrained scope of exploration. Addressing these shortcomings necessitates the development of innovative exploration methods or devices. This article introduces Stratloong, a novel underwater exploration robot designed specifically for drilling in seabed stratum. Comprising a drill bit, front and rear support units, and a propulsion unit, Stratloong emulates the peristaltic motion of an earthworm to achieve efficient drilling. In this research, kinematic and dynamic models of the robot are formulated, and a task-based control method based on inverse kinematic control is presented. In addition, a generic motion control framework is proposed to realize the drilling motion. Straight drilling tests are conducted in prepared seabed clay under different static settlement times to assess Stratloong's performance. Data collected include rotational speed, displacement, and axial force during motion. The robot maintained over 90% motion efficiency in the prepared seabed clay. Furthermore, outdoor tests confirmed the robot's ability to drill into soil without external thrust. The robot advanced 2230 mm with 89% motion efficiency. The comprehensive evaluation of Stratloong's drilling capabilities, conducted through a series of laboratory and field tests, yields valuable data and experiences for its potential application in seabed strata exploration.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"763-774"},"PeriodicalIF":3.8,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-29DOI: 10.1109/JOE.2024.3384563
Haonan Shi;Haihan Zhao;Zhiwei Zhu;Chao Wang;Haofeng Hu;Jingsheng Zhai;Xiaobo Li
Advanced sensors and signal processing algorithms are significant for the use of remotely-operated vehicles and autonomous underwater vehicles. The distance/length measurement is the basis of many sensing functions, including positioning, tracking, surface reconstruction, and pose determination. Optical-based ranging sensors have been proven as a promising tool and obtain up to micrometer-level accuracy when combined with dual-comb interference. Applying this approach to underwater scenarios is feasible, but one must handle the issue that the ranging signal is significantly affected by environmental disturbances and system noises. However, it has been rarely reported that processing algorithms are tailored to the dual-comb signal to improve the quality of measuring signals. This article presents an enhanced underwater dual-comb ranging (DCR) solution via an improved variational mode decomposition (VMD). Specifically, we design a fitness function considering desired dual-comb interferogram characteristics. Accordingly, we optimize vital parameters and decompose the interested ranging signal to ensure final interferogram quality. Experiments verify that our method is superior to others and can improve the signal-to-noise ratio and restore the Gaussian-like shape of interferograms simultaneously. To the best of the authors knowledge, it is the first time DCR is boosted via VMD, and answers the question about interferogram shaping. The proposed solution may find important applications in ranging and imaging tasks underwater, as well as extend their working range and robustness against non-ideal environments.
{"title":"Enhanced Dual-Comb Underwater Ranging via an Improved VMD Algorithm","authors":"Haonan Shi;Haihan Zhao;Zhiwei Zhu;Chao Wang;Haofeng Hu;Jingsheng Zhai;Xiaobo Li","doi":"10.1109/JOE.2024.3384563","DOIUrl":"10.1109/JOE.2024.3384563","url":null,"abstract":"Advanced sensors and signal processing algorithms are significant for the use of remotely-operated vehicles and autonomous underwater vehicles. The distance/length measurement is the basis of many sensing functions, including positioning, tracking, surface reconstruction, and pose determination. Optical-based ranging sensors have been proven as a promising tool and obtain up to micrometer-level accuracy when combined with dual-comb interference. Applying this approach to underwater scenarios is feasible, but one must handle the issue that the ranging signal is significantly affected by environmental disturbances and system noises. However, it has been rarely reported that processing algorithms are tailored to the dual-comb signal to improve the quality of measuring signals. This article presents an enhanced underwater dual-comb ranging (DCR) solution via an improved variational mode decomposition (VMD). Specifically, we design a fitness function considering desired dual-comb interferogram characteristics. Accordingly, we optimize vital parameters and decompose the interested ranging signal to ensure final interferogram quality. Experiments verify that our method is superior to others and can improve the signal-to-noise ratio and restore the Gaussian-like shape of interferograms simultaneously. To the best of the authors knowledge, it is the first time DCR is boosted via VMD, and answers the question about interferogram shaping. The proposed solution may find important applications in ranging and imaging tasks underwater, as well as extend their working range and robustness against non-ideal environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"841-855"},"PeriodicalIF":3.8,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-25DOI: 10.1109/JOE.2024.3365799
Jichen Chu;Lei Cheng;Wen Xu
In acoustic array signal processing, spatial power spectrum estimation and the associated direction-of-arrival (DOA) estimation are often inflicted by strong interferences, which lead to significant performance degradation and even mask the weak targets. Although tremendous efforts have been put into related research, simultaneously realizing robust interference suppression and DOA estimation against various model mismatches is still challenging. To address this challenge, this article proposes a systematic scheme that cohesively integrates a robust beamforming step and a beam-space sparse learning step, to effectively recover the spatial power spectrum in the presence of strong interference. Considering the sparsity and nonnegativity of the spatial power spectrum, we propose a nonnegative fast sparse Bayesian learning algorithm to reconstruct the spatial power spectrum of target sources from the beam-space data. In addition to the outstanding interference suppression capabilities, our method exhibits better denoising performance (i.e., lower noise level) and DOA estimation accuracy, even under challenging scenarios, such as snapshot deficiency and low signal-to-noise ratios. Simulated and real-life experimental data results verify the robustness and superior performance of the proposed scheme over other competitors.
在声学阵列信号处理中，空间功率谱估计和相关的到达方向（DOA）估计经常受到强干扰的影响，导致性能显著下降，甚至掩盖弱目标。尽管相关研究已付出巨大努力，但同时实现鲁棒的干扰抑制和 DOA 估计以应对各种模型失配仍具有挑战性。为了应对这一挑战，本文提出了一种系统方案，将鲁棒波束成形步骤和波束空间稀疏学习步骤有机地结合在一起，从而在强干扰下有效地恢复空间功率谱。考虑到空间功率谱的稀疏性和非负性，我们提出了一种非负快速稀疏贝叶斯学习算法，从波束空间数据中重建目标源的空间功率谱。除了出色的干扰抑制能力，我们的方法还表现出更好的去噪性能（即更低的噪声水平）和 DOA 估计精度，即使在快照不足和低信噪比等具有挑战性的情况下也是如此。模拟和实际实验数据结果验证了所提方案的鲁棒性和优于其他竞争对手的性能。
{"title":"Spatial Power Spectrum Estimation Under Strong Interferences Using Beam-Space Fast Nonnegative Sparse Bayesian Learning","authors":"Jichen Chu;Lei Cheng;Wen Xu","doi":"10.1109/JOE.2024.3365799","DOIUrl":"10.1109/JOE.2024.3365799","url":null,"abstract":"In acoustic array signal processing, spatial power spectrum estimation and the associated direction-of-arrival (DOA) estimation are often inflicted by strong interferences, which lead to significant performance degradation and even mask the weak targets. Although tremendous efforts have been put into related research, simultaneously realizing robust interference suppression and DOA estimation against various model mismatches is still challenging. To address this challenge, this article proposes a systematic scheme that cohesively integrates a robust beamforming step and a beam-space sparse learning step, to effectively recover the spatial power spectrum in the presence of strong interference. Considering the sparsity and nonnegativity of the spatial power spectrum, we propose a nonnegative fast sparse Bayesian learning algorithm to reconstruct the spatial power spectrum of target sources from the beam-space data. In addition to the outstanding interference suppression capabilities, our method exhibits better denoising performance (i.e., lower noise level) and DOA estimation accuracy, even under challenging scenarios, such as snapshot deficiency and low signal-to-noise ratios. Simulated and real-life experimental data results verify the robustness and superior performance of the proposed scheme over other competitors.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"692-712"},"PeriodicalIF":3.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1109/JOE.2024.3379481
Xin Wen;Feihu Zhang;Chensheng Cheng;Xujia Hou;Guang Pan
Side-scan sonar (SSS) plays a crucial role in underwater exploration. Autonomous analysis of SSS images is vital for detecting unknown targets in underwater environments. However, due to the complexity of the underwater environment, few highlighted areas of the target, blurred feature details, and the difficulty of collecting data from SSS, achieving high-precision autonomous target recognition in SSS images is challenging. This article solves this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in SSS images. First, we enhance and enlarge real and experimental images using the denoising–diffusion model to establish a self-made SSS image data set, as there are data pictures of the detection target in the SSS images obtained from real experiments. Since the SSS image has large areas without targets, this article introduces a vision transformer (ViT) for dynamic attention and global modeling, which improves the model's weight in the target region. Second, the convolutional block attention module is adopted to further improve the feature expression ability and reduce floating-point operations. Finally, this article uses Scylla-Intersection over Union as the loss function to increase the accuracy of the model's inference. Experiments on the SSS image data set demonstrate that the improved YOLOv7 model outperforms other technologies, with an average accuracy (mAP0.5) and (mAP0.5:0.95) of 78.00% and 48.11%, respectively. These results are 3.47% and 2.9% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this article has great potential for object detection and recognition of SSS images.
侧扫声纳（SSS）在水下探测中发挥着至关重要的作用。自主分析 SSS 图像对于探测水下环境中的未知目标至关重要。然而，由于水下环境的复杂性、目标高亮区域少、特征细节模糊以及 SSS 数据采集困难等原因，在 SSS 图像中实现高精度自主目标识别具有挑战性。本文通过改进 You Only Look Once v7（YOLOv7）模型来解决这一问题，从而实现 SSS 图像中的高精度目标检测。首先，我们利用去噪扩散模型对真实图像和实验图像进行增强和放大，建立一个自制的 SSS 图像数据集，因为在真实实验获得的 SSS 图像中存在检测目标的数据图片。由于 SSS 图像中有大片区域没有目标，本文引入了视觉变换器（ViT）进行动态关注和全局建模，提高了模型在目标区域的权重。其次，采用卷积块注意力模块，进一步提高特征表达能力，减少浮点运算。最后，本文采用 Scylla-Intersection over Union 作为损失函数，提高了模型推理的准确性。在 SSS 图像数据集上的实验表明，改进后的 YOLOv7 模型优于其他技术，其平均准确率（mAP0.5）和（mAP0.5:0.95）分别为 78.00% 和 48.11%。这些结果比 YOLOv7 模型分别高出 3.47% 和 2.9%。本文提出的改进型 YOLOv7 算法在 SSS 图像的物体检测和识别方面具有很大的潜力。
{"title":"Side-Scan Sonar Underwater Target Detection: Combining the Diffusion Model With an Improved YOLOv7 Model","authors":"Xin Wen;Feihu Zhang;Chensheng Cheng;Xujia Hou;Guang Pan","doi":"10.1109/JOE.2024.3379481","DOIUrl":"10.1109/JOE.2024.3379481","url":null,"abstract":"Side-scan sonar (SSS) plays a crucial role in underwater exploration. Autonomous analysis of SSS images is vital for detecting unknown targets in underwater environments. However, due to the complexity of the underwater environment, few highlighted areas of the target, blurred feature details, and the difficulty of collecting data from SSS, achieving high-precision autonomous target recognition in SSS images is challenging. This article solves this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in SSS images. First, we enhance and enlarge real and experimental images using the denoising–diffusion model to establish a self-made SSS image data set, as there are data pictures of the detection target in the SSS images obtained from real experiments. Since the SSS image has large areas without targets, this article introduces a vision transformer (ViT) for dynamic attention and global modeling, which improves the model's weight in the target region. Second, the convolutional block attention module is adopted to further improve the feature expression ability and reduce floating-point operations. Finally, this article uses Scylla-Intersection over Union as the loss function to increase the accuracy of the model's inference. Experiments on the SSS image data set demonstrate that the improved YOLOv7 model outperforms other technologies, with an average accuracy (mAP0.5) and (mAP0.5:0.95) of 78.00% and 48.11%, respectively. These results are 3.47% and 2.9% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this article has great potential for object detection and recognition of SSS images.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"976-991"},"PeriodicalIF":3.8,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motion estimation is required to obtain high imaging quality in synthetic aperture sonars (SASs). Displaced phase center antenna (DPCA) micronavigation is an important technique of motion estimation in SASs. A key step in DPCA micronavigation is accurately determining the time delay between echoes received by the approximate “phase center” array between adjacent pings. Unfortunately, the accuracy of the existing method for estimating this time delay is often deteriorated by the ambiguity of the time delay estimates in the presence of noise or interference. This article proposes an accurate method of estimating the time delay based on the Robust Chinese Remainder Theorem (RCRT). The experimental results show that the proposed method decreases the ambiguous rate of time delay estimates by one order of magnitude, compared to the conventional approach, which means the estimation accuracy is improved significantly. The SAS imaging results demonstrate that the RCRT-based motion estimation helps to obtain higher-quality images.
{"title":"Robust Chinese Remainder Theorem–Based Synthetic Aperture Sonar Motion Estimation","authors":"Cheng Chi;Shiping Chen;Rongxing Zhong;Pengfei Zhang;Peng Wang;Yu Li;Jiyuan Liu;Haining Huang","doi":"10.1109/JOE.2023.3328084","DOIUrl":"10.1109/JOE.2023.3328084","url":null,"abstract":"Motion estimation is required to obtain high imaging quality in synthetic aperture sonars (SASs). Displaced phase center antenna (DPCA) micronavigation is an important technique of motion estimation in SASs. A key step in DPCA micronavigation is accurately determining the time delay between echoes received by the approximate “phase center” array between adjacent pings. Unfortunately, the accuracy of the existing method for estimating this time delay is often deteriorated by the ambiguity of the time delay estimates in the presence of noise or interference. This article proposes an accurate method of estimating the time delay based on the Robust Chinese Remainder Theorem (RCRT). The experimental results show that the proposed method decreases the ambiguous rate of time delay estimates by one order of magnitude, compared to the conventional approach, which means the estimation accuracy is improved significantly. The SAS imaging results demonstrate that the RCRT-based motion estimation helps to obtain higher-quality images.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"933-943"},"PeriodicalIF":3.8,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140170248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study delves into the question of whether the mooring system influences the dynamics of the device by conducting a comprehensive analysis of the inertial sea wave energy converter (ISWEC). Recognizing that wave energy converters exhibit complex behaviors that often push numerical models beyond their range of validity, this study highlights the importance of developing a representative model that accurately captures the intricate dynamics involved. To address this challenge, an experimental investigation of the ISWEC is conducted, aiming to establish a benchmark model that serves as a reference for validating and refining numerical models. Following the experimental investigation, this study proceeds with a numerical investigation to further explore the influence of the mooring system on the pitching device. The response of the device is analyzed both with and without the mooring system, allowing for a direct comparison of its effects on device dynamics and the associated harvested energy. By conducting numerical simulations under various operating conditions, this study provides an insight into the definition of representative mathematical modeling, analyzing and motivating the strong influence of the mooring system on the performances of a moored pitching wave energy conversion system.
{"title":"Experimental and Numerical Investigation on the Performance of a Moored Pitching Wave Energy Conversion System","authors":"Bruno Paduano;Fabio Carapellese;Edoardo Pasta;Mauro Bonfanti;Sergej Antonello Sirigu;Dario Basile;Domenica Pizzirusso;Nicoláas Faedo;Giuliana Mattiazzo","doi":"10.1109/JOE.2024.3353372","DOIUrl":"10.1109/JOE.2024.3353372","url":null,"abstract":"This study delves into the question of whether the mooring system influences the dynamics of the device by conducting a comprehensive analysis of the inertial sea wave energy converter (ISWEC). Recognizing that wave energy converters exhibit complex behaviors that often push numerical models beyond their range of validity, this study highlights the importance of developing a representative model that accurately captures the intricate dynamics involved. To address this challenge, an experimental investigation of the ISWEC is conducted, aiming to establish a benchmark model that serves as a reference for validating and refining numerical models. Following the experimental investigation, this study proceeds with a numerical investigation to further explore the influence of the mooring system on the pitching device. The response of the device is analyzed both with and without the mooring system, allowing for a direct comparison of its effects on device dynamics and the associated harvested energy. By conducting numerical simulations under various operating conditions, this study provides an insight into the definition of representative mathematical modeling, analyzing and motivating the strong influence of the mooring system on the performances of a moored pitching wave energy conversion system.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"802-820"},"PeriodicalIF":3.8,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10473602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140170337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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