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Intelligent Optimization for Marine Vehicle Navigation and Positioning
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Intelligent Optimization for Marine Vehicle Navigation and Positioning

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Navigation and Positioning".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4546

Editors

Dr. Xiaoyang Gao

E-Mail
Guest Editor
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
Interests: marine vehicle systems; dynamic positioning; navigation planning; optimized control; intelligent control
Dr. Ke Zhang

E-Mail Website
Guest Editor
Department of Intelligence, China Waterborne Transport Research Institute, Beijing 100088, China
Interests: intelligent navigation; autonomous decision-making and control; collision avoidance for autonomous ship; risk analysis of ship collision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the actual operation of marine vehicle systems, the navigation and positioning functions are highly susceptible to interference from multiple complex factors. These disturbances include not only environmental disturbances such as wind, wave, and current loads, but also internal perturbations like sensor measurement noise and actuator hysteresis, with a significant strong coupling effect existing among various types of disturbance.

These constraints directly affect the operational stability of navigation and positioning systems, reduce the accuracy and reliability of sensor data acquisition, further restrict the operational efficiency of marine vehicles, and threaten the operational safety of related tasks such as offshore exploration and maritime transportation. Therefore, it is imperative to develop intelligent optimized navigation and positioning strategies integrated with advanced sensor technologies.

Currently, solutions such as learning-driven optimal trajectory planning schemes and sensor adaptive calibration control strategies have been gradually applied in this field. In addition, the practical implementation of these optimized schemes can promote the integration and lightweight development of navigation and positioning systems and sensors, reduce equipment deployment, operation and maintenance costs, and yield remarkable engineering value and economic benefits. The call for papers for this Special Issue aims to establish an academic exchange platform for researchers and practitioners in related fields worldwide, focusing on the latest theoretical research, technological breakthroughs, and engineering application achievements in the field of sensor-enabled intelligent optimized navigation and positioning for marine vehicle systems so as to facilitate technological innovation and achievement transformation in this field.

Dr. Xiaoyang Gao
Dr. Ke Zhang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • marine vehicle systems
  • dynamic positioning
  • navigation planning
  • optimized control
  • intelligent control

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Published Papers (6 papers)

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Research

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29 pages, 13607 KB  
Article
A Path Planning Method for Intelligent Ships Based on the Improved Artificial Potential Field Algorithm
by Xiao Liu, Hua Deng, Xingya Zhao, Kexin Xu and Deqing Yu
Sensors 2026, 26(14), 4569; https://doi.org/10.3390/s26144569 - 19 Jul 2026
Viewed by 441
Abstract
Path planning for unmanned ships has become an important research topic in recent years. To enhance navigation safety and reduce collision risk, this study proposes an improved artificial potential field (IAPF) method. A route gravitational force is introduced to guide the ship back [...] Read more.
Path planning for unmanned ships has become an important research topic in recent years. To enhance navigation safety and reduce collision risk, this study proposes an improved artificial potential field (IAPF) method. A route gravitational force is introduced to guide the ship back to the planned route after collision avoidance, while the repulsive force is optimized to improve path smoothness and obstacle-avoidance stability. A collision-risk-index-based repulsive force is further developed for dynamic obstacle avoidance, and its direction is modified according to the COLREGs. In addition, a time-sequential rolling path-planning framework integrating the APF and velocity obstacle algorithms is proposed to suppress path oscillation and adapt to target-ship maneuvers. The method is validated in head-on, crossing, and multiple-obstacle scenarios. The minimum passing distances are 1064.07 m, 1072.98 m, and 1109.66 m, respectively, and the maximum decision time is 148 ms. The results demonstrate that the proposed method can generate smooth, COLREGs-compliant paths, avoid local minima, adapt to dynamic encounters, and satisfy real-time collision-avoidance requirements. Full article
(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)
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22 pages, 4162 KB  
Article
An Online Detection and Rejection Method for Consecutive Outliers in Underwater Long-Baseline Positioning Based on Kinematic Constraints
by Le Wang, Jun Su, Runze Mao and Sha Wang
Sensors 2026, 26(13), 4013; https://doi.org/10.3390/s26134013 - 24 Jun 2026
Viewed by 376
Abstract
To address the issue of persistent high-magnitude outlier interference affecting long-baseline (LBL) positioning systems in complex marine environments, this paper proposes a kinematic constraint-based Robust Interacting Multiple Model Kalman Filter algorithm. Combined with anchor point initialization and multi-step historical observations, the algorithm constructs [...] Read more.
To address the issue of persistent high-magnitude outlier interference affecting long-baseline (LBL) positioning systems in complex marine environments, this paper proposes a kinematic constraint-based Robust Interacting Multiple Model Kalman Filter algorithm. Combined with anchor point initialization and multi-step historical observations, the algorithm constructs a spatial Euclidean distance discriminant criterion. By further incorporating the maximum velocity constraint of the Autonomous Underwater Vehicle (AUV), dynamic decision thresholds are established, and final detection decisions are output to the positioning system. Within the Kalman Filter recursion process, a measurement mask matrix is introduced to instantly isolate measurement outliers, preventing abnormal data from participating in state updates and model probability evolution. Simulation results demonstrate that, compared with standard LBL positioning, conventional single outlier detection, and the conventional maximum correntropy criterion-based Kalman filter (MCC-KF) algorithm, the proposed approach enhances outlier identification and suppression—particularly under consecutive anomaly conditions—thereby improving the positioning accuracy of maneuvering targets in complex underwater scenarios. Full article
(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)
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25 pages, 18317 KB  
Article
Dynamic Object Detection in Maritime Navigation Scenarios Based on Vision–Radar Fusion
by Qianqian Chen, Changshi Xiao and Bowei Li
Sensors 2026, 26(11), 3508; https://doi.org/10.3390/s26113508 - 2 Jun 2026
Viewed by 476
Abstract
With the rapid development of intelligent navigation technologies, accurate dynamic object detection in complex maritime environments remains a critical challenge due to occlusion, scale variation, and multi-target interference. To address these issues, this study proposes a vision–radar fusion-based dynamic object detection method. A [...] Read more.
With the rapid development of intelligent navigation technologies, accurate dynamic object detection in complex maritime environments remains a critical challenge due to occlusion, scale variation, and multi-target interference. To address these issues, this study proposes a vision–radar fusion-based dynamic object detection method. A cross-modal feature mapping mechanism is developed to achieve deep integration of visual and radar information, and an augmented Lagrangian optimization strategy is introduced to enhance feature consistency and representation capability. Furthermore, an improved Faster R-CNN framework is designed by optimizing the region proposal network and incorporating a multi-scale training strategy to improve detection performance for objects of varying scales. Experimental results on a self-constructed MVRD show that the proposed method achieves detection accuracies of 88.93%, 76.86%, 74.47%, and 83.01% under sunny, strong illumination, foggy, and crossing-waterway conditions, respectively. These results demonstrate that the proposed approach exhibits strong robustness and stability in complex maritime environments. Overall, the method significantly improves dynamic object detection accuracy and provides effective support for reliable environmental perception in intelligent navigation systems. Full article
(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)
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20 pages, 2324 KB  
Article
A System Identification Approach to Motion Model Based on Full-Scale Ship Maneuvering Data
by Yanfei Tian, Wuliu Tian, Ke Zhang, Lin Hua, Jie Wen and Fangyang Zhu
Sensors 2026, 26(10), 3199; https://doi.org/10.3390/s26103199 - 19 May 2026
Viewed by 788
Abstract
The paper concerns motion modeling for full-scale ships under the frame of system identification (SI) principles. Several groups of full-scale ship maneuvering experiments have been implemented to collect research data. On structure identification, as an innovation, a nonlinear integrating ship motion model is [...] Read more.
The paper concerns motion modeling for full-scale ships under the frame of system identification (SI) principles. Several groups of full-scale ship maneuvering experiments have been implemented to collect research data. On structure identification, as an innovation, a nonlinear integrating ship motion model is identified and established. The concerned model includes 21 parameters. Under the premise of error criterion, a batch least-squares (BLS)-based parameter estimation process is used to estimate the 21 parameters. The strategy is verified for feasibility and availability by using a pragmatic case study. The accuracy of the estimated parameter values is checked by comparing the track in simulation with the trial trajectory. Research indicates that the technical process proposed in the paper from the perspective of SI principles can be applied to the modeling of ship maneuvering motion. Full article
(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)
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21 pages, 4632 KB  
Article
An Enhanced Event-Based Model for Integrated Flight Safety of Fixed-Wing UAVs
by Xin Ma, Xikang Lu, Hongwei Li, Xiyue Lu, Jiahua Li and Jiajun Zhao
Sensors 2026, 26(7), 2058; https://doi.org/10.3390/s26072058 - 25 Mar 2026
Viewed by 671
Abstract
To address the issues of safety risk analysis and conflict assessment for integrated flight of manned aircraft and fixed-wing unmanned aerial vehicles (UAVs) in low-altitude mixed-operation airspace, this study enhances the foundational Event model. By incorporating UAV characteristics such as geometric features and [...] Read more.
To address the issues of safety risk analysis and conflict assessment for integrated flight of manned aircraft and fixed-wing unmanned aerial vehicles (UAVs) in low-altitude mixed-operation airspace, this study enhances the foundational Event model. By incorporating UAV characteristics such as geometric features and aerodynamic mechanisms, alongside design dimensions and onboard performance metrics, an improved collision risk model is developed—the Enhanced Event-Based Framework for Multidimensional Geometry and Quasi-Monte Carlo Analysis of Flight Performance (EMGF-M). This enhancement rectifies the limitations of the basic model regarding parameter coverage and scenario adaptability, thereby improving the reliability and validity of the computational results. Experimental results demonstrate that, in accordance with the target safety level for airspace conflicts set by the International Civil Aviation Organization (ICAO), the application of the improved Event collision model yields quantifiable assessments of safety risks and safe separation distances for integrated operations in low-altitude mixed-use airspace. Utilizing these computational results for integrated flight procedure design at a general airport in Southwest China, the study shows that the air traffic flow in the low-altitude mixed-operation airspace increased from 9.2 to 20.9 operations per hour. The practical significance of this method lies in its guidance for accurately assessing safety risks in mixed airspace operations and for determining quantifiable separation minima for integrated flight trajectory planning. Full article
(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)
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Review

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25 pages, 742 KB  
Review
Advances in Optimized and Safe Path Planning of Marine Autonomous Surface Vehicles: A Review
by Lirong Kou and Xiaoyang Gao
Sensors 2026, 26(11), 3445; https://doi.org/10.3390/s26113445 - 29 May 2026
Cited by 1 | Viewed by 698
Abstract
With the rapid development of intelligent shipping and the autonomy of marine engineering equipment, numerous studies have focused on the advancement of Autonomous Surface Vehicles (ASVs). As a fundamental component of ASV automation systems, path planning directly determines the safety and economy of [...] Read more.
With the rapid development of intelligent shipping and the autonomy of marine engineering equipment, numerous studies have focused on the advancement of Autonomous Surface Vehicles (ASVs). As a fundamental component of ASV automation systems, path planning directly determines the safety and economy of ship navigation. This paper systematically reviews recent research progress in ASV path planning. First, five key issues are identified for ASV path planning: navigation environment, environment modeling, ship motion model, collision avoidance for safety, and optimization. Second, existing algorithms are classified into four categories: graph search-based, sampling-based, numerical optimization-based, and artificial intelligence-based. The improvement directions and application scenarios of each category are elaborated. Finally, the four types of algorithms are evaluated against three indicators: path quality, scalability and extensibility, and algorithm performance. Analysis of the reviewed literature shows that traditional graph search and sampling algorithms perform well in various aspects under static environments, but are insufficient in adapting to multiple constraints and generalizing to different environments. In contrast, artificial intelligence algorithms represented by deep reinforcement learning exhibit significant advantages in dynamic collision avoidance decision-making, multi-agent coordination, and environmental generalization, and have become the mainstream direction of current research. This paper summarizes the existing challenges in safety and optimization in current ASV path planning research and prospects future development directions. Full article
(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)
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