This article focuses on the optimization of marine hydrokinetic farms of coaxial dual-rotor turbines with wake interaction. To perform the optimization, we introduce a new analytical wake model for this turbine configuration and validate it herein. The proposed model predicts the wake velocity deficit in the near- and far-wake of the turbine in terms of the diameters and axial induction factors of the upstream and downstream rotors and the location of the near-wake boundary. It is derived by utilizing mass- and momentum balancing in the near- and far-wake control volumes, supplemented by the application of Bernoulli's principle along pertinent streamlines. The analytical prediction is compared with computational simulation results for different flow conditions to find good agreement between them. The optimization problem is solved by the implementation of a genetic algorithm, which is developed based on the wake model. The algorithm maximizes farm efficiency by minimizing the wake interactions among the turbines. The influence of different parameters of the algorithm on its overall performance and efficiency is investigated to discover that a perfect integration among the parameters is essential for a successful search. Eventually, three different cases are studied with different farm sizes, numbers of cells in farm layouts, and aspect ratios of the farm at each of the flow conditions to illustrate the functionality and robustness of the algorithm that is based on the proposed wake model. The optimization results will be useful for the assessment of the hydrokinetic power potential of such turbine configurations in an ocean or riverine current.
{"title":"Marine Hydrokinetic Farm Optimization for Coaxial Dual-Rotor Turbines","authors":"Mehedi Hassan;Matthew Bryant;Andre Mazzoleni;Praveen Ramaprabhu;Kenneth Granlund","doi":"10.1109/JOE.2024.3393538","DOIUrl":"10.1109/JOE.2024.3393538","url":null,"abstract":"This article focuses on the optimization of marine hydrokinetic farms of coaxial dual-rotor turbines with wake interaction. To perform the optimization, we introduce a new analytical wake model for this turbine configuration and validate it herein. The proposed model predicts the wake velocity deficit in the near- and far-wake of the turbine in terms of the diameters and axial induction factors of the upstream and downstream rotors and the location of the near-wake boundary. It is derived by utilizing mass- and momentum balancing in the near- and far-wake control volumes, supplemented by the application of Bernoulli's principle along pertinent streamlines. The analytical prediction is compared with computational simulation results for different flow conditions to find good agreement between them. The optimization problem is solved by the implementation of a genetic algorithm, which is developed based on the wake model. The algorithm maximizes farm efficiency by minimizing the wake interactions among the turbines. The influence of different parameters of the algorithm on its overall performance and efficiency is investigated to discover that a perfect integration among the parameters is essential for a successful search. Eventually, three different cases are studied with different farm sizes, numbers of cells in farm layouts, and aspect ratios of the farm at each of the flow conditions to illustrate the functionality and robustness of the algorithm that is based on the proposed wake model. The optimization results will be useful for the assessment of the hydrokinetic power potential of such turbine configurations in an ocean or riverine current.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1411-1429"},"PeriodicalIF":3.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777524","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 : 2024-07-22DOI: 10.1109/JOE.2024.3408888
Hongyue Chen;Zhongrui Zhu;Desen Yang
We investigate the beamforming method for detecting signals and estimating their directions of arrival for an acoustic vector-sensor linear array mounted near a baffle. The array-measurement model is established based on the baffle reflection coefficient, which is calculated using transfer matrices and matched boundary conditions. Herein, we propose a conventional beamforming (CBF) algorithm based on polarization filtering. First, the CBF output is directly decomposed into a sum of polarization monochromatic signals by the quaternion Fourier transform. Next, polarization filtering is conducted on each monochromatic signal and is regarded as a postprocessor to the CBF algorithm. This filtering in the polarization domain can improve the signal-to-noise ratio of the CBF algorithm. The proposed algorithm produces a lower noise level than the CBF algorithm. Moreover, the performance of this algorithm is related to the degree of polarization of the noise, and the best performance is obtained as the degree of polarization approaches zero. In this study, the performance of the proposed algorithm is evaluated by simulations and experiments.
{"title":"Conventional Beamforming Algorithm Based on Polarization Filtering for an Acoustic Vector-Sensor Linear Array Mounted Near a Baffle","authors":"Hongyue Chen;Zhongrui Zhu;Desen Yang","doi":"10.1109/JOE.2024.3408888","DOIUrl":"10.1109/JOE.2024.3408888","url":null,"abstract":"We investigate the beamforming method for detecting signals and estimating their directions of arrival for an acoustic vector-sensor linear array mounted near a baffle. The array-measurement model is established based on the baffle reflection coefficient, which is calculated using transfer matrices and matched boundary conditions. Herein, we propose a conventional beamforming (CBF) algorithm based on polarization filtering. First, the CBF output is directly decomposed into a sum of polarization monochromatic signals by the quaternion Fourier transform. Next, polarization filtering is conducted on each monochromatic signal and is regarded as a postprocessor to the CBF algorithm. This filtering in the polarization domain can improve the signal-to-noise ratio of the CBF algorithm. The proposed algorithm produces a lower noise level than the CBF algorithm. Moreover, the performance of this algorithm is related to the degree of polarization of the noise, and the best performance is obtained as the degree of polarization approaches zero. In this study, the performance of the proposed algorithm is evaluated by simulations and experiments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1127-1139"},"PeriodicalIF":3.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777546","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 : 2024-07-22DOI: 10.1109/JOE.2024.3401968
Ciaran J. Sanford;Benjamin W. Thomas;Alan J. Hunter
This article introduces a new method for simulating synthetic aperture sonar (SAS) raw coherent echo data, which is orders of magnitude faster than the commonly used point and facet diffraction models. The new approach uses Fourier wavefield generation and propagation in combination with a highly optimized optical rendering engine. It has been shown to produce a quantifiably similar quality of data and data products (i.e., images and spectra) to a point-diffraction model, capturing the important coherent wave physics (including diffraction, speckle, aspect-dependence, and layover) as well as effects of the SAS processing chain (including image focusing errors and artifacts). This new simulation capability may be an enabler for augmenting data sets with physically accurate and diverse synthetic data for robust machine learning.
本文介绍了一种模拟合成孔径声纳（SAS）原始相干回波数据的新方法，该方法比常用的点和面衍射模型快几个数量级。新方法使用傅立叶波场生成和传播，并结合高度优化的光学渲染引擎。结果表明，它生成的数据和数据产品（即图像和光谱）的质量在数量上与点衍射模型相似，能捕捉到重要的相干波物理现象（包括衍射、斑点、方位依赖性和分层）以及 SAS 处理链的影响（包括图像聚焦误差和伪影）。这一新的模拟能力将有助于利用物理上准确且多样化的合成数据来增强数据集，从而实现强大的机器学习。
{"title":"Fourier-Domain Wavefield Rendering for Rapid Simulation of Synthetic Aperture Sonar Data","authors":"Ciaran J. Sanford;Benjamin W. Thomas;Alan J. Hunter","doi":"10.1109/JOE.2024.3401968","DOIUrl":"10.1109/JOE.2024.3401968","url":null,"abstract":"This article introduces a new method for simulating synthetic aperture sonar (SAS) raw coherent echo data, which is orders of magnitude faster than the commonly used point and facet diffraction models. The new approach uses Fourier wavefield generation and propagation in combination with a highly optimized optical rendering engine. It has been shown to produce a quantifiably similar quality of data and data products (i.e., images and spectra) to a point-diffraction model, capturing the important coherent wave physics (including diffraction, speckle, aspect-dependence, and layover) as well as effects of the SAS processing chain (including image focusing errors and artifacts). This new simulation capability may be an enabler for augmenting data sets with physically accurate and diverse synthetic data for robust machine learning.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1501-1515"},"PeriodicalIF":3.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777525","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 : 2024-07-22DOI: 10.1109/JOE.2024.3386227
Mohamed Barbary;Mohamed H. Abd ElAzeem
This article presents the application of a track before detect (TBD) technique for multiple extended object tracking (EOT) in a heavy-tailed cluttered environment using a high-resolution marine inverse synthetic aperture radar system. In high sea states, the ship EOTs make complex maneuvering motions due to strong disturbances, such as sea waves and sea winds. In this work, we utilize emergent maneuvering EOT (M-EOT) methodologies in real-time scenarios based on the popular multi-Bernoulli (MB)-TBD filter, and in particular, we describe the ship M-EOT's state through the subrandom matrices model (sub-RMM). In sub-RMM, scatter centers are distributed symmetrically around the M-EOT's centroid. However, in the ship M-EOT scenario, the distribution over the whole object is not symmetrical, but distributed and skewed in some portions while a target maneuvers. To solve this problem, a novel robustness observation model is represented using a nonsymmetrically skewed normal distribution and multiple model MB-TBD with more than one ellipse. Simulation and experimental results illustrate that the proposed filter outperforms the existing filters for M-EOTs.
{"title":"Robust and Flexible Maritime ISAR Tracking Algorithm for Multiple Maneuvering Extended Vessels in Heavy-Tailed Clutter Using Skewed Multiple Model MB-Sub-RMM-TBD Filter","authors":"Mohamed Barbary;Mohamed H. Abd ElAzeem","doi":"10.1109/JOE.2024.3386227","DOIUrl":"10.1109/JOE.2024.3386227","url":null,"abstract":"This article presents the application of a track before detect (TBD) technique for multiple extended object tracking (EOT) in a heavy-tailed cluttered environment using a high-resolution marine inverse synthetic aperture radar system. In high sea states, the ship EOTs make complex maneuvering motions due to strong disturbances, such as sea waves and sea winds. In this work, we utilize emergent maneuvering EOT (M-EOT) methodologies in real-time scenarios based on the popular multi-Bernoulli (MB)-TBD filter, and in particular, we describe the ship M-EOT's state through the subrandom matrices model (sub-RMM). In sub-RMM, scatter centers are distributed symmetrically around the M-EOT's centroid. However, in the ship M-EOT scenario, the distribution over the whole object is not symmetrical, but distributed and skewed in some portions while a target maneuvers. To solve this problem, a novel robustness observation model is represented using a nonsymmetrically skewed normal distribution and multiple model MB-TBD with more than one ellipse. Simulation and experimental results illustrate that the proposed filter outperforms the existing filters for M-EOTs.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1233-1264"},"PeriodicalIF":3.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777547","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 : 2024-06-20DOI: 10.1109/JOE.2024.3401969
Dalong Zhang;Shuai Chang;Guoji Zou;Chengcheng Wan;Hui Li
Due to the position drift of inertial navigation systems, it is still challenging to achieve long-term and accurate position estimates during underwater navigation. The seabed topography has been proven to be effective in aiding information for accurate positioning benefiting from its rich spatial variation. With the advantage of the multibeam echosounder (MBES) in efficient bathymetric survey, the simultaneous localization and mapping (SLAM) approach can be performed using bathymetric data in unknown environments for underwater vehicles to get good position estimates. The SLAM performance relies on the number and accuracy of loop closures heavily. Thereby, the capabilities of the data association method and solver in dealing with the uncertainties of vehicle pose estimates, bathymetric data, and topographic features affect the SLAM performance strongly. This work proposes a new graph-based bathymetric SLAM method to improve the robustness of the uncertainties in both factor-graph construction and optimization stages. In the front end, on the base of a matching suitability-based MBES submap construction method, a dual-stage bathymetric point cloud registration approach that is able to detect most false loop closures is proposed. In the back end, a robust optimizer based on Frechet distance is introduced to further identify and remove the false loop closures missed in front end. Experiments using field MBES bathymetric data sets are conducted to verify the effectiveness of the proposed approach.
由于惯性导航系统的位置漂移，在水下导航过程中实现长期和准确的位置估计仍然具有挑战性。海底地形因其丰富的空间变化而被证明是精确定位的有效辅助信息。利用多波束回声测深仪（MBES）在高效水深测量方面的优势，可以在未知环境中使用水深测量数据对水下航行器进行同步定位和绘图（SLAM），以获得良好的位置估计。SLAM 的性能主要取决于闭环的数量和精度。因此，数据关联方法和求解器在处理车辆姿态估计、水深数据和地形特征的不确定性方面的能力对 SLAM 性能影响很大。本研究提出了一种新的基于图的测深 SLAM 方法，以提高因子图构建和优化阶段对不确定性的鲁棒性。在前端，在基于匹配适宜性的 MBES 子图构建方法的基础上，提出了一种双阶段测深点云注册方法，该方法能够检测到大多数错误的环路闭合。在后端，引入了基于 Frechet 距离的稳健优化器，以进一步识别和消除前端漏掉的错误环路闭合。利用实地 MBES 测深数据集进行了实验，以验证所提方法的有效性。
{"title":"A Robust Graph-Based Bathymetric Simultaneous Localization and Mapping Approach for AUVs","authors":"Dalong Zhang;Shuai Chang;Guoji Zou;Chengcheng Wan;Hui Li","doi":"10.1109/JOE.2024.3401969","DOIUrl":"10.1109/JOE.2024.3401969","url":null,"abstract":"Due to the position drift of inertial navigation systems, it is still challenging to achieve long-term and accurate position estimates during underwater navigation. The seabed topography has been proven to be effective in aiding information for accurate positioning benefiting from its rich spatial variation. With the advantage of the multibeam echosounder (MBES) in efficient bathymetric survey, the simultaneous localization and mapping (SLAM) approach can be performed using bathymetric data in unknown environments for underwater vehicles to get good position estimates. The SLAM performance relies on the number and accuracy of loop closures heavily. Thereby, the capabilities of the data association method and solver in dealing with the uncertainties of vehicle pose estimates, bathymetric data, and topographic features affect the SLAM performance strongly. This work proposes a new graph-based bathymetric SLAM method to improve the robustness of the uncertainties in both factor-graph construction and optimization stages. In the front end, on the base of a matching suitability-based MBES submap construction method, a dual-stage bathymetric point cloud registration approach that is able to detect most false loop closures is proposed. In the back end, a robust optimizer based on Frechet distance is introduced to further identify and remove the false loop closures missed in front end. Experiments using field MBES bathymetric data sets are conducted to verify the effectiveness of the proposed approach.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1350-1370"},"PeriodicalIF":3.8,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507577","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 : 2024-06-20DOI: 10.1109/JOE.2024.3388101
Brian M. Emery;Anthony Kirincich
We use the radar equation along with in situ observations of Bragg-resonant ocean waves to estimate the scattering patch area for each radial velocity observation from a direction finding high frequency (HF) radar operating at 13 Mhz. Estimated areas for range cells 2–10 (3–15 km) vary from less than 1 km $^{2}$ to more than 10 km $^{2}$ , with a mean of 2.5 km $^{2}$ and standard deviation of 2.3 km $^{2}$ . Assuming a 1.8 km effective range cell width, and given the known ranges, these are approximately equivalent to a mean angular width of 8.5 $^text{o}$ , and a standard deviation of 10.0 $^{circ }$ . The scattering patch areas follow a Weibull distribution, with scale and shape parameters 2.5 and 1.2. Patch area uncertainties, expressed as a percentage of the patch area, average 36%, with most below 50%. Knowledge of the scattering patch area could allow improved data combining methods (e.g., appropriate weighting or variable resolution totals), and improve the ability of HF radar networks to resolve small scale flows and eddies. Understanding the scattering patch size may also contribute to improved HF radar-based observations of coastal winds, as it plays a role in the relationship between wind speed and backscattered power.
{"title":"Estimating Scattering Patch Area for a Direction Finding HF Radar","authors":"Brian M. Emery;Anthony Kirincich","doi":"10.1109/JOE.2024.3388101","DOIUrl":"10.1109/JOE.2024.3388101","url":null,"abstract":"We use the radar equation along with in situ observations of Bragg-resonant ocean waves to estimate the scattering patch area for each radial velocity observation from a direction finding high frequency (HF) radar operating at 13 Mhz. Estimated areas for range cells 2–10 (3–15 km) vary from less than 1 km\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 to more than 10 km\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000, with a mean of 2.5 km\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 and standard deviation of 2.3 km\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000. Assuming a 1.8 km effective range cell width, and given the known ranges, these are approximately equivalent to a mean angular width of 8.5\u0000<inline-formula><tex-math>$^text{o}$</tex-math></inline-formula>\u0000, and a standard deviation of 10.0\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000. The scattering patch areas follow a Weibull distribution, with scale and shape parameters 2.5 and 1.2. Patch area uncertainties, expressed as a percentage of the patch area, average 36%, with most below 50%. Knowledge of the scattering patch area could allow improved data combining methods (e.g., appropriate weighting or variable resolution totals), and improve the ability of HF radar networks to resolve small scale flows and eddies. Understanding the scattering patch size may also contribute to improved HF radar-based observations of coastal winds, as it plays a role in the relationship between wind speed and backscattered power.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1199-1208"},"PeriodicalIF":3.8,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10565988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507575","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}
Pub Date : 2024-06-20DOI: 10.1109/JOE.2024.3402816
Geunhwan Kim;Youngmin Choo
Anomaly detection is suitable for active sonar classification due to its ability to handle the challenges posed by small imbalanced data sets. Recently, a modified anomaly detection approach called bisphere anomaly detection (BiSAD) has been developed for active sonar classification and has demonstrated improved generalization performance over conventional deep-learning-based methods. However, BiSAD has some limitations: multimodalities of clutter distribution induce unnecessary redundancy in the clutter manifold, and the inconsistency of two encoder outputs causes instability during learning. We propose a modified version of BiSAD called multisphere anomaly detection (MulSAD), which incorporates clustering and regularization. Clustering is used to model the multimodal distribution of the clutter samples, whereas regularization ensures consistency in the manifold learning of the two encoders. Two active sonar data sets generated in two different ocean experiments with different environments are used alternatively as the training/validation and test data sets. The efficacy of the modifications is confirmed by analyzing the classification performance according to hyperparameters. In the generalization test, MulSAD outperforms both the supervised-learning-based deep learning methods and BiSAD. Furthermore, MulSAD is more robust to mislabeled data samples in the training data sets.
{"title":"Enhancing Generalization of Active Sonar Classification Using Semisupervised Anomaly Detection With Multisphere for Normal Data","authors":"Geunhwan Kim;Youngmin Choo","doi":"10.1109/JOE.2024.3402816","DOIUrl":"10.1109/JOE.2024.3402816","url":null,"abstract":"Anomaly detection is suitable for active sonar classification due to its ability to handle the challenges posed by small imbalanced data sets. Recently, a modified anomaly detection approach called bisphere anomaly detection (BiSAD) has been developed for active sonar classification and has demonstrated improved generalization performance over conventional deep-learning-based methods. However, BiSAD has some limitations: multimodalities of clutter distribution induce unnecessary redundancy in the clutter manifold, and the inconsistency of two encoder outputs causes instability during learning. We propose a modified version of BiSAD called multisphere anomaly detection (MulSAD), which incorporates clustering and regularization. Clustering is used to model the multimodal distribution of the clutter samples, whereas regularization ensures consistency in the manifold learning of the two encoders. Two active sonar data sets generated in two different ocean experiments with different environments are used alternatively as the training/validation and test data sets. The efficacy of the modifications is confirmed by analyzing the classification performance according to hyperparameters. In the generalization test, MulSAD outperforms both the supervised-learning-based deep learning methods and BiSAD. Furthermore, MulSAD is more robust to mislabeled data samples in the training data sets.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1530-1548"},"PeriodicalIF":3.8,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507576","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}
Multiarray systems are widely used in sonar and radar applications. They can improve communication speeds, target discrimination, and imaging. In the case of a multibeam sonar system that can operate two receiving arrays, we derive new adaptive tests to improve detection capabilities compared to traditional sonar detection approaches. To do so, we more specifically consider correlated arrays, whose covariance matrices are estimated up to scale factors, and an impulsive clutter. In a partially homogeneous environment, the two-step generalized likelihood ratio test and Rao approach lead to a generalization of the adaptive normalized matched filter test and an equivalent numerically simpler detector with a well-established texture constant false alarm rate behavior. Performances are discussed and illustrated with theoretical examples, numerous simulations, and insights into experimental data. Results show that these detectors outperform their competitors and have stronger robustness to environmental unknowns.
{"title":"Robust Detection for Mills Cross Sonar","authors":"Olivier Lerda;Ammar Mian;Guillaume Ginolhac;Jean-Philippe Ovarlez;Didier Charlot","doi":"10.1109/JOE.2024.3374958","DOIUrl":"https://doi.org/10.1109/JOE.2024.3374958","url":null,"abstract":"Multiarray systems are widely used in sonar and radar applications. They can improve communication speeds, target discrimination, and imaging. In the case of a multibeam sonar system that can operate two receiving arrays, we derive new adaptive tests to improve detection capabilities compared to traditional sonar detection approaches. To do so, we more specifically consider correlated arrays, whose covariance matrices are estimated up to scale factors, and an impulsive clutter. In a partially homogeneous environment, the two-step generalized likelihood ratio test and Rao approach lead to a generalization of the adaptive normalized matched filter test and an equivalent numerically simpler detector with a well-established texture constant false alarm rate behavior. Performances are discussed and illustrated with theoretical examples, numerous simulations, and insights into experimental data. Results show that these detectors outperform their competitors and have stronger robustness to environmental unknowns.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 3","pages":"1009-1024"},"PeriodicalIF":3.8,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630975","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 : 2024-06-07DOI: 10.1109/JOE.2024.3381391
Jesus Guerrero;Ahmed Chemori;Vincent Creuze;Jorge Torres
When an autonomous underwater vehicle is performing missions in the ocean, it is often subject to external disturbances, such as sea currents and changes in salinity. These phenomena can degrade the performance of the controller of the vehicle, which can increase the tracking error or cause instability. Taking into account these issues, in this article, we design an adaptive controller based on high-order sliding mode control and focus on the paradigm of the trajectory tracking of underwater vehicles. First, we rewrite the classical representation of the underwater vehicle in terms of the known dynamics, and then transform it into a control affine structure. Then, we design an adaptive high-order sliding mode controller for trajectory tracking. Also, we prove the stability of the resulting closed-loop system using Lyapunov arguments. Finally, real-time experiments are performed to validate the proposed methodology, as well as its robustness.
{"title":"Improved Adaptive High-Order Sliding Mode-Based Control for Trajectory Tracking of Autonomous Underwater Vehicles","authors":"Jesus Guerrero;Ahmed Chemori;Vincent Creuze;Jorge Torres","doi":"10.1109/JOE.2024.3381391","DOIUrl":"10.1109/JOE.2024.3381391","url":null,"abstract":"When an autonomous underwater vehicle is performing missions in the ocean, it is often subject to external disturbances, such as sea currents and changes in salinity. These phenomena can degrade the performance of the controller of the vehicle, which can increase the tracking error or cause instability. Taking into account these issues, in this article, we design an adaptive controller based on high-order sliding mode control and focus on the paradigm of the trajectory tracking of underwater vehicles. First, we rewrite the classical representation of the underwater vehicle in terms of the known dynamics, and then transform it into a control affine structure. Then, we design an adaptive high-order sliding mode controller for trajectory tracking. Also, we prove the stability of the resulting closed-loop system using Lyapunov arguments. Finally, real-time experiments are performed to validate the proposed methodology, as well as its robustness.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 4","pages":"1337-1349"},"PeriodicalIF":3.8,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945675","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 : 2024-06-04DOI: 10.1109/JOE.2024.3369148
Murad Tukan;Eli Biton;Roee Diamant
Water current prediction is essential for understanding ecosystems, and to shed light on the role of the ocean in the global climate context. Solutions vary from physical modeling, and long-term observations, to short-term measurements. In this article, we consider a common approach for water current prediction that uses Lagrangian floaters for water current prediction by interpolating the trajectory of the elements to reflect the velocity field. Here, an important aspect that has not been addressed before is where to initially deploy the drifting elements such that the acquired velocity field would efficiently represent the water current. To that end, we use a clustering approach that relies on a physical model of the velocity field. Our method segments the modeled map and determines the deployment locations as those that will lead the floaters to “visit” the center of the different segments. This way, we validate that the area covered by the floaters will capture the inhomogeneity of the velocity field. Exploration over a dataset of velocity field maps that span over a year demonstrates the applicability of our approach, and shows a considerable improvement over the common approach of uniformly randomly choosing the initial deployment sites. We share our Python implementation code for reproducibility in (Jubran et al., 2022).
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