IEEE Journal of Oceanic Engineering最新文献_第4页

Adaptive Refocusing Chain for Moving Ships in Satellite SAR Images 卫星SAR图像中移动舰船的自适应重聚焦链

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-26 DOI: 10.1109/JOE.2025.3529210

Seung-Jae Lee

{"title":"Adaptive Refocusing Chain for Moving Ships in Satellite SAR Images","authors":"Seung-Jae Lee","doi":"10.1109/JOE.2025.3529210","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529210","url":null,"abstract":"In this study, an adaptive refocusing scheme for moving ships in satellite synthetic aperture radar (SAR) images is proposed to cope with various types of motions of ship targets. To decide the type of ship's motion, the phase signals of principal scatterers are analyzed based on the inverse SAR (ISAR) signal model with the help of a joint time–frequency transform and deep learning model. Then, proper ISAR-based refocusing algorithms are used to generate a well-focused image considering the ship's motion. The design of the adaptive refocusing concept enables us to select appropriate algorithms to retrieve the exact scattering mechanisms of ship targets. In addition, to cope with defocusing due to the complex 3-D motion of the ship, an efficient reconstruction strategy based on compressive sensing is devised. It is a concept different from conventional optimal time windowing, which deals with the complex motion of the ship target, and it yields a well-focused image that retains the spatial resolution of the original ship image. In experiments using simulated and real SAR images, the proposed method shows reliable refocusing results for various ship targets compared to traditional methods.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1290-1308"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852451","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}

引用次数: 0

Design, Development, and Testing of an Innovative Autonomous Underwater Reconfigurable Vehicle for Versatile Applications 设计、开发和测试用于多种应用的创新型自主水下可重构飞行器

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-26 DOI: 10.1109/JOE.2024.3511709

Mirco Vangi;Edoardo Topini;Gherardo Liverani;Alberto Topini;Alessandro Ridolfi;Benedetto Allotta

{"title":"Design, Development, and Testing of an Innovative Autonomous Underwater Reconfigurable Vehicle for Versatile Applications","authors":"Mirco Vangi;Edoardo Topini;Gherardo Liverani;Alberto Topini;Alessandro Ridolfi;Benedetto Allotta","doi":"10.1109/JOE.2024.3511709","DOIUrl":"https://doi.org/10.1109/JOE.2024.3511709","url":null,"abstract":"The underwater industry and scientific community are actively researching the development of vehicles that combine the functionalities of autonomous underwater vehicles and remotely operated vehicles. An innovative approach to address the challenges posed by underwater exploration is the development of autonomous underwater reconfigurable vehicles (AURVs). These vehicles are designed to adapt their configuration to suit the requirements of the task at hand. The flexibility of AURVs enables them to undertake a variety of underwater missions, ranging from scientific research to deep-sea exploration. The Department of Industrial Engineering at the University of Florence, Italy, has developed and patented an innovative AURV that is able to quickly change its shape to suit different tasks. The reconfigurable underwater vehicle for inspection, free-floating intervention and survey tasks (RUVIFIST) have been equipped with two extreme configurations. The first configuration is a slender one meant for long navigation tasks, while the second configuration is a stocky one designed for tackling complex objectives such as inspection or intervention operations. With the ability to adapt its form to suit the task at hand, the RUVIFIST vehicle represents a significant advancement in underwater vehicle technology. This work provides an overview of the challenges faced and the solutions adopted during the development of this new vehicle. This article presents the results of experimental campaigns to test the reconfigurable system of the vehicle and the strategies developed for the guidance, navigation, and control system of AURVs. Finally, preliminary tests were conducted to explore the integration of machine learning and deep learning algorithms that are compatible with the purpose of automatic target recognition.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"509-526"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852373","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}

引用次数: 0

Hilbert–Huang Transform With Intelligent Noise Reduction for Passive SONAR Signal Processing 基于智能降噪的Hilbert-Huang变换被动声纳信号处理

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-21 DOI: 10.1109/JOE.2024.3519737

Elio Pithon Sarno Filho;Anderson Damacena Santos;Eduardo F. Simas Filho;Antonio Carlos Lopes Fernandes;José Manoel de Seixas;Natanael N. de Moura

{"title":"Hilbert–Huang Transform With Intelligent Noise Reduction for Passive SONAR Signal Processing","authors":"Elio Pithon Sarno Filho;Anderson Damacena Santos;Eduardo F. Simas Filho;Antonio Carlos Lopes Fernandes;José Manoel de Seixas;Natanael N. de Moura","doi":"10.1109/JOE.2024.3519737","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519737","url":null,"abstract":"Ocean science plays a key role in marine exploration, encouraging the development of new methods for analyzing underwater acoustic waves. In passive SOund NAvigation and Ranging (SONAR) signal processing for military vessel detection and classification, the predominant technique is the short-time Fourier transform (STFT). However, this spectral analysis method has time–frequency (TF) resolution limitations, impacting performance in feature extraction and vessel dynamic behavior monitoring. The Hilbert–Huang transform (HHT) is an alternative to STFT, providing a data-driven TF analysis with high resolution. However, in standard HHT algorithms, estimation accuracy degrades as noise increases. This article presents a novel algorithm for HHT that computes the HHT with intelligent noise removal (HHT-INR). The proposed method is focused on passive SONAR surveillance applications, in which the information of interest usually comprises different sinusoidal components produced by the vessels' machinery and propeller system. An intelligent system based on support vector machine detects and removes noisy IMF during the EMD estimation process. Results with simulated and experimental passive SONAR signals indicate better performance than the STFT-based analysis. The HHT-INR reduces background noise and enhances resolution for analyzing vessel parameters in time-varying scenarios. The proposed method significantly improved frequency resolution in experimental signals, achieving an average reduction in spectral width of approximately 28.5 times. In addition, there was an average increase of 87.9 dB in the signal-to-noise ratio.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1387-1402"},"PeriodicalIF":3.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860763","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}

引用次数: 0

Sea Surface Wave Retrieval From C-Band Sentinel-1 Images in the Arctic Ocean 从北冰洋 C 波段哨兵-1 图像中检索海面波浪

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-19 DOI: 10.1109/JOE.2024.3519738

Yuyi Hu;Weizeng Shao;Maurizio Migliaccio;Ferdinando Nunziata;Qingjun Zhang

{"title":"Sea Surface Wave Retrieval From C-Band Sentinel-1 Images in the Arctic Ocean","authors":"Yuyi Hu;Weizeng Shao;Maurizio Migliaccio;Ferdinando Nunziata;Qingjun Zhang","doi":"10.1109/JOE.2024.3519738","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519738","url":null,"abstract":"The Arctic Ocean presents significant challenges for estimating sea surface wave fields using <italic>C</i>-band synthetic aperture radar (SAR) due to the distortion caused by the reflection of sea ice. This article introduces a novel procedure to successfully consider the influence of sea ice in SAR wave retrieval at latitude <80°.>K</i>-means clustering algorithm was applied to estimate sea ice concentration from the images. Using 1000 images in the training data set, the tilt mapping model transfer functions (MTFs) in VV and HH polarization are generated under various sea ice concentration conditions. Then, a theoretical wave retrieval algorithm, namely, the parameterized first-guess spectrum method, that uses the updated tilt MTF was implemented for an additional 600 images in a test data set for wave retrieval in the Arctic Ocean. Compression of the SAR-derived SWHs and WW3 simulations yields an RMSE of 0.45 m, a COR of 0.91, a bias of 0.38 m, and an SI of 0.11 using the updated tilt MTF, which is an improvement upon the RMSE of 0.60 m, a bias of 0.41 m, a COR of 0.88, and an SI of 0.14 obtained using the previous tilt MTF. Moreover, the accuracy of VV-polarized SAR-derived SWH by using the updated tilt MTF is improved by approximately 0.15-m RMSE and 0.08-m bias, which is based on validating SAR-derived SWHs against the measurements from the HY-2B altimeter. However, the noise in the retrievals still needs further mitigation.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1259-1272"},"PeriodicalIF":3.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852464","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}

引用次数: 0

CA-Net: Cascaded Adaptive Network for Underwater Image Enhancement ca网：用于水下图像增强的级联自适应网络

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-14 DOI: 10.1109/JOE.2024.3501399

Xiaofei Zhou;Ming Peng;Qiuping Jiang;Runmin Cong;Jiyong Wang;Yun Chen

{"title":"CA-Net: Cascaded Adaptive Network for Underwater Image Enhancement","authors":"Xiaofei Zhou;Ming Peng;Qiuping Jiang;Runmin Cong;Jiyong Wang;Yun Chen","doi":"10.1109/JOE.2024.3501399","DOIUrl":"https://doi.org/10.1109/JOE.2024.3501399","url":null,"abstract":"Due to light absorption and scattering, underwater images often suffer from low contrast, blurry details, and color deviation. Various enhancement methods have been developed, but many fail to improve image quality effectively and sometimes create unnatural effects. To tackle such a problem, we propose a novel method, namely the Cascaded Adaptive Network (i.e., CA-Net), to comprehensively enhance the quality of underwater images. Specifically, our network adopts a cascaded enhancement architecture consisting of three stages (coarse feature restoration, feature aggregation, and color refinement). First, we use a detail restoration (DR) module and channel balance module to recover spatial details and correct color distortion, respectively, in the first stage. Particularly, the detail guidance unit of DR employs encoder features to steer the decoder features to focus more on the spatial details of objects. Second, to promote the fusion of fine details and color features, we deploy a context attention (CA) module and an adaptive feature fusion (AFF) module in the stage of feature aggregation. CA extracts detailed restoration features and long-range dependencies in images, guiding the fusion process in the subsequent AFF. Lastly, to guarantee natural colors, we use a global color rendering module in the stage of color refinement, which adaptively groups and tunes the image channels. Experiments on public data sets show that CA-Net significantly outperforms existing methods, making it highly effective for underwater image enhancement.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"879-897"},"PeriodicalIF":3.8,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848832","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}

引用次数: 0

UIEFormer: Lightweight Vision Transformer for Underwater Image Enhancement UIEFormer：用于水下图像增强的轻型视觉变压器

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-13 DOI: 10.1109/JOE.2024.3519681

Juntian Qu;Xiangyu Cao;Shancheng Jiang;Jia You;Zhenping Yu

{"title":"UIEFormer: Lightweight Vision Transformer for Underwater Image Enhancement","authors":"Juntian Qu;Xiangyu Cao;Shancheng Jiang;Jia You;Zhenping Yu","doi":"10.1109/JOE.2024.3519681","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519681","url":null,"abstract":"The selective absorption and scattering of light in water degrade underwater image quality, hindering the performance of underwater tasks. Moreover, existing data-driven underwater image enhancement (UIE) methods rely on large-scale, high-quality underwater image data sets, which are costly to acquire in terms of time and labor. In this work, we present a UIE framework named UIEFormer, which is built upon a popular conventional image defogging framework DehazeFormer, possessing satisfactory performance on a small-scale training data set of underwater images. We propose an interpolation-based upsampling strategy to avoid checkerboard artifacts caused by PixelShuffle. Extra feature channels are introduced to segregate noncritical high-level image features for UIE tasks. Further, we apply a loss function combining per-pixel loss, perceptual loss, and coloration loss to adapt to the underwater environment. Results on real-world data sets demonstrate that our method has certain advantages over classical and popular UIE methods. In addition, we conduct ablation experiments to demonstrate the contribution of each module in our work. We also demonstrate the practical significance of our approach for underwater image processing tasks.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"851-865"},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848784","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}

引用次数: 0

Underwater Image Enhancement Using Illuminant Intensity Compensation With Foreground Edge Map Rectification 用光强度补偿和前景边缘图校正的水下图像增强

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-11 DOI: 10.1109/JOE.2024.3523372

Herng-Hua Chang;Pin-Yi Kuan

{"title":"Underwater Image Enhancement Using Illuminant Intensity Compensation With Foreground Edge Map Rectification","authors":"Herng-Hua Chang;Pin-Yi Kuan","doi":"10.1109/JOE.2024.3523372","DOIUrl":"https://doi.org/10.1109/JOE.2024.3523372","url":null,"abstract":"Underwater image enhancement has been paid more attention in recent years as it is a fundamental task in many relevant image processing applications. This article investigates a new underwater image enhancement algorithm based on a simplified image formation model established by the integration of the Jaffe–McGlamery and Lambertian systems. The retinex theory is introduced into the prototype to explicitly disclose the illuminant intensity, which is computed using an efficient gray index scheme for light source attenuation compensation. Subsequently, an improved scene depth estimation method is exploited to separate the foreground from the background, upon which a foreground edge map is computed for better background light determination. Finally, an ensemble color gain is appraised to correct the color deviation. A wide variety of underwater images with various scenarios in six different data sets were employed to evaluate the proposed image enhancement system. Experimental results demonstrated the advantages of our underwater image enhancement algorithm over many state-of-the-art methods both qualitatively and quantitatively. It is believed that the developed image enhancement framework has potential in many underwater image processing applications.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"835-850"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848827","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}

引用次数: 0

DenseNet-Based Robust Channel Estimation in OFDM for Improving Underwater Acoustic Communication 基于密度的OFDM鲁棒信道估计改善水声通信

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-11 DOI: 10.1109/JOE.2024.3510929

Songzuo Liu;Muhamamd Adil;Lu Ma;Suleman Mazhar;Gang Qiao

{"title":"DenseNet-Based Robust Channel Estimation in OFDM for Improving Underwater Acoustic Communication","authors":"Songzuo Liu;Muhamamd Adil;Lu Ma;Suleman Mazhar;Gang Qiao","doi":"10.1109/JOE.2024.3510929","DOIUrl":"https://doi.org/10.1109/JOE.2024.3510929","url":null,"abstract":"Underwater acoustic (UWA) communication presents unique challenges due to the unpredictable and dynamic nature of acoustic channels, influenced by Doppler spread, low signal-to-noise ratios (SNRs), and the general need for complex channel characteristics, coupled with a scarcity of real-world data. Accurate orthogonal frequency division multiplexing (OFDM) channel estimation is pivotal for ensuring reliable data transmission in such challenging environments. In this study, we introduce the DenseNet estimator, which is specifically used for OFDM channel estimation in UWA communication. The use of dense connectivity within the DenseNet structure proves to be advantageous in capturing the intricacies of the complex and dynamic UWA channels. This architecture, showcasing robustness even when there's a limited number of pilots, sets it apart from conventional methods. The DenseNet estimator is trained on the WATERMARK data set, leveraging the richness of real-time varying channel impulse responses to provide the necessary diversity for accurate channel estimation. Uniquely, once trained, our DenseNet estimator operates without necessitating additional channel statistics like SNR, relying solely on the received signal as its primary input. This approach offers a simplified and more direct application in real-world scenarios. Our numerical results underscore the DenseNet estimator's efficacy: It consistently outperforms traditional methods such as least square, minimum mean square error, and fully connected neural network, recording improvements of up to 96.3%, 94.2%, and 40.0% in bit error rate. Performance assessments across various watermark underwater channels demonstrate the DenseNet estimator's adaptability and robustness in both stable and challenging environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1518-1537"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852407","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}

引用次数: 0

Utility-Centered Underwater Image Quality Evaluation 以实用为中心的水下图像质量评价

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-11 DOI: 10.1109/JOE.2024.3498273

Weiling Chen;Honggang Liao;Rongfu Lin;Tiesong Zhao;Ke Gu;Patrick Le Callet

{"title":"Utility-Centered Underwater Image Quality Evaluation","authors":"Weiling Chen;Honggang Liao;Rongfu Lin;Tiesong Zhao;Ke Gu;Patrick Le Callet","doi":"10.1109/JOE.2024.3498273","DOIUrl":"https://doi.org/10.1109/JOE.2024.3498273","url":null,"abstract":"In recent decades, the emergence of image applications has greatly facilitated the development of vision-based tasks. As a result, image quality assessment (IQA) has become increasingly significant for monitoring, controlling, and improving visual signal quality. While existing IQA methods focus on image fidelity and aesthetics to characterize perceived quality, it is important to evaluate the utility-centered quality of an image for popular tasks, such as object detection. However, research shows that there is a low correlation between utilities and perceptions. To address this issue, this article proposes a utility-centered IQA approach. Specifically, our research focuses on underwater fish detection as a challenging task in an underwater environment. Based on this task, we have developed a utility-centered underwater image quality database (UIQD) and a transfer learning-based advanced underwater quality by utility assessment (AQUA). Inspired by the top–down design approach used in fidelity-oriented IQA methods, we utilize deep models of object detection and transfer their features to the mission of utility-centered quality evaluation. Experimental results validate that the proposed AQUA achieves promising performance not only in fish detection but also in other tasks such as face recognition. We believe that our research provides valuable insights to bridge the gap between IQA research and visual tasks.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"743-757"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848811","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}

引用次数: 0

Feasibility Study on the Elimination of Force-Position Coupling in Steering Systems With a Novel Compound Rotary Vane Steering Gear 一种新型复合转叶舵机消除转向系统力位耦合的可行性研究

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-10 DOI: 10.1109/JOE.2024.3516094

Chang Yuan;Jianxing Zhang;Baoren Li;Yuxuan Peng;Zhaozhuo Wang

{"title":"Feasibility Study on the Elimination of Force-Position Coupling in Steering Systems With a Novel Compound Rotary Vane Steering Gear","authors":"Chang Yuan;Jianxing Zhang;Baoren Li;Yuxuan Peng;Zhaozhuo Wang","doi":"10.1109/JOE.2024.3516094","DOIUrl":"https://doi.org/10.1109/JOE.2024.3516094","url":null,"abstract":"In this article, a novel compound rotary vane steering gear actuator was designed to solve the problem of strong force-position coupling between the rudder blade and hydrodynamic force during the steering process. The actuator applies active torque to the rudder drive cylinder through a torque decoupling cylinder, so as to eliminate the load torque generated by the hydraulic force on the rudder drive cylinder. The simulation and experiment results show that compared with a single-layer rotary vane steering gear, the compound rotary vane steering gear has faster steering speed, higher position accuracy, and stronger disturbance rejection capability under the influence of hydrodynamic loads. Under disturbances of hydrodynamic load, the average time for the compound rotary vane steering gear to reach steady state is reduced by 37.45%, and the steady-state error is less than 0.1°. When the impact load is encountered, the average stability time is reduced by 41.45%, thus verifying the principle of eliminating load by structure. The compound rotary vane steering gear demonstrated excellent maneuvering performance when applied to steering systems with large inertia and strong nonlinearity.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1196-1209"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852434","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}

引用次数: 0