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5 pages, 184 KiB

Open AccessEditorial

Remote Sensing in Vessel Detection and Navigation

by Henning Heiselberg and Andrzej Stateczny

Sensors 2020, 20(20), 5841; https://doi.org/10.3390/s20205841 - 15 Oct 2020

Cited by 12 | Viewed by 2708

Abstract

The Special Issue (SI) “Remote Sensing in Vessel Detection and Navigation” highlighted a variety of topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments [...] Read more.

The Special Issue (SI) “Remote Sensing in Vessel Detection and Navigation” highlighted a variety of topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used. The 15 papers (from 23 submitted) were published. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

Research

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18 pages, 714 KiB

Open AccessArticle

A Detection and Tracking Algorithm for Resolvable Group with Structural and Formation Changes Using the Gibbs-GLMB Filter

by Xinfeng Ru, Yudong Chi and Weifeng Liu

Sensors 2020, 20(12), 3384; https://doi.org/10.3390/s20123384 - 15 Jun 2020

Cited by 6 | Viewed by 2473

Abstract

In the field of resolvable group target tracking, further study on the structure and formation of group targets is helpful to reduce the tracking error of group bluetargets. In this paper, we propose an algorithm to detect whether the structure or formation state [...] Read more.

In the field of resolvable group target tracking, further study on the structure and formation of group targets is helpful to reduce the tracking error of group bluetargets. In this paper, we propose an algorithm to detect whether the structure or formation state of group targets changes. In this paper, a Gibbs Generalized Labeled Multi-Bernoulli (GLMB) filter is used to obtain the estimation of discernible group target bluestates. After obtaining the state estimation of the group target, we extract relevant information based on the estimation data to judge whether the structure or formation state has changed. Finally, several experiments are carried out to verify the algorithm. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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19 pages, 14061 KiB

Open AccessArticle

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

by Haitao Dong, Ke He, Xiaohong Shen, Shilei Ma, Haiyan Wang and Changcheng Qiao

Sensors 2020, 20(11), 3269; https://doi.org/10.3390/s20113269 - 8 Jun 2020

Cited by 12 | Viewed by 2963

Abstract

Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. [...] Read more.

Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter

a_{o p t} = 1

. As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to

H_{1}

(periodic signal with noise) and

H_{0}

(pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD). Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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21 pages, 9000 KiB

Open AccessArticle

Extremely Robust Remote-Target Detection Based on Carbon Dioxide-Double Spikes in Midwave Spectral Imaging

by Sungho Kim, Jungsub Shin, Joonmo Ahn and Sunho Kim

Sensors 2020, 20(10), 2896; https://doi.org/10.3390/s20102896 - 20 May 2020

Cited by 5 | Viewed by 2646

Abstract

Infrared ship-target detection for sea surveillance from the coast is very challenging because of strong background clutter, such as cloud and sea glint. Conventional approaches utilize either spatial or temporal information to reduce false positives. This paper proposes a completely different approach, called [...] Read more.

Infrared ship-target detection for sea surveillance from the coast is very challenging because of strong background clutter, such as cloud and sea glint. Conventional approaches utilize either spatial or temporal information to reduce false positives. This paper proposes a completely different approach, called carbon dioxide-double spike (CO₂-DS) detection in midwave spectral imaging. The proposed CO₂-DS is based on the spectral feature where a hot CO₂ emission band is broader than that which is absorbed by normal atmospheric CO₂, which generates CO₂-double spikes. A directional-mean subtraction filter (D-MSF) detects each CO₂ spike, and final targets are detected by joint analysis of both types of detection. The most important property of CO₂-DS detection is that it generates an extremely low number of false positive caused by background clutter. Only the hot CO₂ spike of a ship plume can penetrate atmosphere, and furthermore, there are only ship CO₂ plume signatures in the double spikes of different spectral bands. Experimental results using midwave Fourier transform infrared (FTIR) in a remote sea environment validate the extreme robustness of the proposed ship-target detection. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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16 pages, 7454 KiB

Open AccessArticle

A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background

by Wenxin Dai, Yuqing Mao, Rongao Yuan, Yijing Liu, Xuemei Pu and Chuan Li

Sensors 2020, 20(9), 2547; https://doi.org/10.3390/s20092547 - 30 Apr 2020

Cited by 46 | Viewed by 4187

Abstract

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address [...] Read more.

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom–up and top–down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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17 pages, 8896 KiB

Open AccessArticle

MSR2N: Multi-Stage Rotational Region Based Network for Arbitrary-Oriented Ship Detection in SAR Images

by Zhenru Pan, Rong Yang and Zhimin Zhang

Sensors 2020, 20(8), 2340; https://doi.org/10.3390/s20082340 - 20 Apr 2020

Cited by 43 | Viewed by 3368

Abstract

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high [...] Read more.

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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13 pages, 1554 KiB

Open AccessArticle

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

by Dawid Polap and Marta Wlodarczyk-Sielicka

Sensors 2020, 20(6), 1608; https://doi.org/10.3390/s20061608 - 13 Mar 2020

Cited by 17 | Viewed by 3078

Abstract

The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of [...] Read more.

The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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16 pages, 1794 KiB

Open AccessArticle

The Single-Shore-Station-Based Position Estimation Method of an Automatic Identification System

by Yi Jiang and Kai Zheng

Sensors 2020, 20(6), 1590; https://doi.org/10.3390/s20061590 - 12 Mar 2020

Cited by 6 | Viewed by 3042

Abstract

In order to overcome the vulnerability of the Global Navigation Satellite System (GNSS), the International Maritime Organization (IMO) initiated the ranging mode (R-Mode) of the automatic identification system (AIS) to provide resilient position data. As the existing AIS is a communication system, the [...] Read more.

In order to overcome the vulnerability of the Global Navigation Satellite System (GNSS), the International Maritime Organization (IMO) initiated the ranging mode (R-Mode) of the automatic identification system (AIS) to provide resilient position data. As the existing AIS is a communication system, the number of shore stations as reference stations cannot satisfy positioning requirements. Especially in the area near a shore station, it is very common that a vessel can only receive signals from one shore station, where the traditional positioning method cannot be used. A novel position estimation method using multiple antennas on shipborne equipment is proposed here, which provides a vessel’s position even though the vessel can only receive signals from a single shore station. It is beneficial for solving positioning issues in proximity to the coast. Further, as the distances between different antennas to the shore station are not sufficiently independent, the positioning matrix can easily be near singularity or ill-conditioned; thus, an effective position solving method is derived. Furthermore, the proposed method is verified and evaluated in different scenarios by numerical simulation. We assessed the influencing factors of positioning performance, such as the vessel’s heading angle, the relative position, and the distances between the shore station and the vessel. The proposed method widely expands the application scope of the AIS R-Mode positioning system. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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23 pages, 8339 KiB

Open AccessArticle

AMARO—An On-Board Ship Detection and Real-Time Information System

by Katharina Willburger, Kurt Schwenk and Jörg Brauchle

Sensors 2020, 20(5), 1324; https://doi.org/10.3390/s20051324 - 29 Feb 2020

Cited by 14 | Viewed by 4320

Abstract

The monitoring of worldwide ship traffic is a field of high topicality. Activities like piracy, ocean dumping, and refugee transportation are in the news every day. The detection of ships in remotely sensed data from airplanes, drones, or spacecraft contributes to maritime situational [...] Read more.

The monitoring of worldwide ship traffic is a field of high topicality. Activities like piracy, ocean dumping, and refugee transportation are in the news every day. The detection of ships in remotely sensed data from airplanes, drones, or spacecraft contributes to maritime situational awareness. However, the crucial factor is the up-to-dateness of the extracted information. With ground-based processing, the time between image acquisition and delivery of the extracted product data is in the range of several hours, mainly due to the time consumed by storing and transmission of the large image data. By processing and analyzing them on-board and transmitting the product data directly as ship position, heading, and velocity, the delay can be shortened to some minutes. Real-time connections via satellite telecommunication services allow small packets of information to be sent directly to the user without significant delay. The AMARO (Autonomous Real-Time Detection of Moving Maritime Objects) project at DLR is a feasibility study of an on-board ship detection system involving on-board processing and real-time communication. The operation of a prototype system was successfully demonstrated on an airborne platform in spring 2018. The on-ground user could be informed about detected vessels within minutes after sighting without a direct communication link. In this article, the scope, aim, and design of the AMARO system are described, and the results of the flight experiment are presented in detail. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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16 pages, 12224 KiB

Open AccessEditor’s ChoiceArticle

Accuracy of Trajectory Tracking Based on Nonlinear Guidance Logic for Hydrographic Unmanned Surface Vessels

by Andrzej Stateczny, Pawel Burdziakowski, Klaudia Najdecka and Beata Domagalska-Stateczna

Sensors 2020, 20(3), 832; https://doi.org/10.3390/s20030832 - 4 Feb 2020

Cited by 36 | Viewed by 4361

Abstract

A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect [...] Read more.

A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect to the measuring profile avoids registration of redundant data and thus saves time and survey costs. This article addresses the issue of precise navigation of the hydrographic unit on the measuring profile with the use of a nonlinear adaptive autopilot. The results of experiments concerning hydrographic measurements performed in real conditions using an USV are discussed. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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25 pages, 5157 KiB

Open AccessArticle

A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile

by Zhequan Fu, Shangsheng Li, Xiangping Li, Bo Dan and Xukun Wang

Sensors 2020, 20(3), 586; https://doi.org/10.3390/s20030586 - 21 Jan 2020

Cited by 13 | Viewed by 2827

Abstract

In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, [...] Read more.

In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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14 pages, 5531 KiB

Open AccessArticle

Vessel Detection and Tracking Method Based on Video Surveillance

by Natalia Wawrzyniak, Tomasz Hyla and Adrian Popik

Sensors 2019, 19(23), 5230; https://doi.org/10.3390/s19235230 - 28 Nov 2019

Cited by 31 | Viewed by 7518

Abstract

Ship detection and tracking is a basic task in any vessel traffic monitored area, whether marine or inland. It has a major impact on navigational safety and thus different systems and technologies are used to determine the best possible methods of detecting and [...] Read more.

Ship detection and tracking is a basic task in any vessel traffic monitored area, whether marine or inland. It has a major impact on navigational safety and thus different systems and technologies are used to determine the best possible methods of detecting and identifying sailing units. Video monitoring is present in almost all of them, but it is usually operated manually and is used as a backup system. This is because of the difficulties in implementing an efficient and universal automatic detection method that would work in quickly alternating environmental conditions for all kind of sailing units—from kayaks to seagoing merchant vessels. This paper presents a method that allows the detection and tracking of ships using the video streams of existing monitoring systems for ports and rivers. The method and the results of experiments on three sets of data using cameras with different characteristics, settings, and scene locations are presented. The experiments were carried out in variable light and weather conditions, and a wide range of unit types were used as detection objectives. The results confirm the usability of the proposed solution; however, some minor issues were encountered in the presence of ships wakes or highly unfavourable weather conditions. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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23 pages, 2784 KiB

Open AccessArticle

Sea–Sky Line and Its Nearby Ships Detection Based on the Motion Attitude of Visible Light Sensors

by Xiongfei Shan, Depeng Zhao, Mingyang Pan, Deqiang Wang and Lining Zhao

Sensors 2019, 19(18), 4004; https://doi.org/10.3390/s19184004 - 16 Sep 2019

Cited by 17 | Viewed by 3731

Abstract

In the maritime scene, visible light sensors installed on ships have difficulty accurately detecting the sea–sky line (SSL) and its nearby ships due to complex environments and six-degrees-of-freedom movement. Aimed at this problem, this paper combines the camera and inertial sensor data, and [...] Read more.

In the maritime scene, visible light sensors installed on ships have difficulty accurately detecting the sea–sky line (SSL) and its nearby ships due to complex environments and six-degrees-of-freedom movement. Aimed at this problem, this paper combines the camera and inertial sensor data, and proposes a novel maritime target detection algorithm based on camera motion attitude. The algorithm mainly includes three steps, namely, SSL estimation, SSL detection, and target saliency detection. Firstly, we constructed the camera motion attitude model by analyzing the camera’s six-degrees-of-freedom motion at sea, estimated the candidate region (CR) of the SSL, then applied the improved edge detection algorithm and the straight-line fitting algorithm to extract the optimal SSL in the CR. Finally, in the region of ship detection (ROSD), an improved visual saliency detection algorithm was applied to extract the target ships. In the experiment, we constructed SSL and its nearby ship detection dataset that matches the camera’s motion attitude data by real ship shooting, and verified the effectiveness of each model in the algorithm through comparative experiments. Experimental results show that compared with the other maritime target detection algorithm, the proposed algorithm achieves a higher detection accuracy in the detection of the SSL and its nearby ships, and provides reliable technical support for the visual development of unmanned ships. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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15 pages, 6150 KiB

Open AccessArticle

Assessment of the Steering Precision of a Hydrographic Unmanned Surface Vessel (USV) along Sounding Profiles Using a Low-Cost Multi-Global Navigation Satellite System (GNSS) Receiver Supported Autopilot

by Mariusz Specht, Cezary Specht, Henryk Lasota and Piotr Cywiński

Sensors 2019, 19(18), 3939; https://doi.org/10.3390/s19183939 - 12 Sep 2019

Cited by 50 | Viewed by 6351

Abstract

The performance of bathymetric measurements by traditional methods (using manned vessels) in ultra-shallow waters, i.e., lakes, rivers, and sea beaches with a depth of less than 1 m, is often difficult or, in many cases, impossible due to problems related to safe vessel [...] Read more.

The performance of bathymetric measurements by traditional methods (using manned vessels) in ultra-shallow waters, i.e., lakes, rivers, and sea beaches with a depth of less than 1 m, is often difficult or, in many cases, impossible due to problems related to safe vessel maneuvering. For this reason, the use of shallow draft hydrographic Unmanned Surface Vessels (USV) appears to provide a promising alternative method for performing such bathymetric measurements. This article describes the modernisation of a USV to switch from manual to automatic mode, and presents a preliminary study aimed at assessing the suitability of a popular autopilot commonly used in Unmanned Aerial Vehicles (UAV), and a low-cost multi-Global Navigation Satellite System (GNSS) receiver cooperating with it, for performing bathymetric measurements in automated mode, which involves independent movement along a specified route (hydrographic sounding profiles). The cross track error (XTE) variable, i.e., the distance determined between a USV’s position and the sounding profile, measured transversely to the course, was adopted as the measure of automatic control precision. Moreover, the XTE value was statistically assessed in the publication. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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14 pages, 3424 KiB

Open AccessArticle

Aircraft and Ship Velocity Determination in Sentinel-2 Multispectral Images

by Henning Heiselberg

Sensors 2019, 19(13), 2873; https://doi.org/10.3390/s19132873 - 28 Jun 2019

Cited by 22 | Viewed by 5262

Abstract

The Sentinel-2 satellites in the Copernicus program provide high resolution multispectral images, which are recorded with temporal offsets up to 2.6 s. Moving aircrafts and ships are therefore observed at different positions due to the multispectral band offsets, from which velocities can be [...] Read more.

The Sentinel-2 satellites in the Copernicus program provide high resolution multispectral images, which are recorded with temporal offsets up to 2.6 s. Moving aircrafts and ships are therefore observed at different positions due to the multispectral band offsets, from which velocities can be determined. We describe an algorithm for detecting aircrafts and ships, and determining their speed, heading, position, length, etc. Aircraft velocities are also affected by the parallax effect and jet streams, and we show how the altitude and the jet stream speed can be determined from the geometry of the aircraft and/or contrail heading. Ship speeds are more difficult to determine as wakes affect the average ship positions differently in the various multispectral bands, and more advanced corrections methods are shown to improve the velocity determination. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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11 pages, 2995 KiB

Open AccessArticle

Sensitivity of Safe Trajectory in a Game Environment on Inaccuracy of Radar Data in Autonomous Navigation

by Józef Lisowski

Sensors 2019, 19(8), 1816; https://doi.org/10.3390/s19081816 - 16 Apr 2019

Cited by 11 | Viewed by 3142

Abstract

This article provides an analysis of the autonomous navigation of marine objects, such as ships, offshore vessels and unmanned vehicles, and an analysis of the accuracy of safe control in game conditions for the cooperation of objects during maneuvering decisions. A method for [...] Read more.

This article provides an analysis of the autonomous navigation of marine objects, such as ships, offshore vessels and unmanned vehicles, and an analysis of the accuracy of safe control in game conditions for the cooperation of objects during maneuvering decisions. A method for determining safe object strategies based on a cooperative multi-person positional modeling game is presented. The method was used to formulate a measure of the sensitivity of safe control in the form of a relative change in the payment of the final game; to determine the final deviation of the safe trajectory from the set trajectory of the autonomous vehicle movement; and to calculate the accuracy of information in terms of evaluating the state of the control process. The sensitivity of safe control was considered in terms of both the degree of the inaccuracy of radar information and changes in the kinematics and dynamics of the object itself. As a result of the simulation studies of the positional game algorithm, which used an example of a real situation at sea of passing one's own object with nine other encountered objects, the sensitivity characteristics of safe trajectories under conditions of both good and restricted visibility at sea are presented. Full article

(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)

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