Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Brčko, Tanja; Androjna, Andrej; Srše, Jure; Boć, R.

doi:10.3390/jmse9010049

Cited by 22 publications

(18 citation statements)

References 27 publications

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“…A frontal anticollision system based on the sensor in question with the additional functionality of maintaining a fixed distance from the detected target has been suggested. An upgrade of the radar navigation system is listed in [11], particularly, its collision avoidance planning tool. In the paper [12], the anti-collision and obstacle avoidance control of moving sensors for a class of distributed parameter systems with a time-varying delay was investigated.…”

Section: Selected Published Research Results In the Field Of Anti-col...mentioning

confidence: 99%

Anti-Collision System for Small Civil Aircraft

2022

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This paper presents the results of the research in the field of anti-collision systems for small civil aircraft, which are not dependent on secondary radars and satellite navigation systems. The aviation communication network was used to design the anti-collision system. The simulation results manifested that the anti-collision system precision depended on the errors of synchronization of the aviation communication network. The precision of the anti-collision system is also influenced by the errors of the coordinates of individual aviation communication network users, based on which the system identifies its own position. The results of the simulation show that the dispersion of the positioning error σ2∆P by the ACS system varied in the range of 1.94 m2 to 503.23 m2. The simulation results confirm that the designed anti-collision system is operational in establishing its position against other FOs, with the distance from the given FOs being 50.0 km maximum. The main contribution of this paper are derived algorithms for the operation of an anti-collision system for small civil aircraft, in addition to the design of movement trajectory models of five flying objects which operate within the aviation communication network. The advantage of the anti-collision system is that it is independent of satnav systems and secondary radars. A significant advantage is the low cost of this system.

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Section: Selected Published Research Results In the Field Of Anti-col...mentioning

confidence: 99%

Anti-Collision System for Small Civil Aircraft

2022

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“…Currently, X-band marine radar has become one of the most widely used marine remote sensors. Generally, X-band marine radar is installed on the ship, which is used for ship positioning, navigation avoidance, and narrow channel navigation [1][2][3]. The wind parameters, such as wind speed and direction, and the wave parameters, such as wave height, direction, and period, can be extracted from the acquired radar images [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

Sun

Wang

et al. 2022

Journal of Sensors

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To suppress the influence of rainfall when extracting sea surface wind and wave parameters using X-band marine radar and control the quality of the collected radar image, it is necessary to detect whether the radar image is contaminated by rainfall. Since the detection accuracy of the statistical characteristics methods (e.g., the zero pixel percentage method and the high-clutter direction method) is limited and the threshold is difficult to determine, the machine learning methods (e.g., the support vector machine-based method and the neural network algorithm) are difficult to select appropriate quality and quantity of data for model training. Therefore, based on the feature that rainfall can change the sea surface texture, a wave texture difference method for rainfall detection is proposed in this paper. Considering the spatial rainfall is uneven, the polar coordinates of the radar image are converted into Cartesian coordinates to detect rainfall. To express the maximum wave difference more accurately, the calculation method of the pixels in the radar texture difference map is redefined. Then, a consecutive pixel method is used to detect the calculated radar texture difference map, and this method can detect adaptively with the change of wind. The data collected from the shore of Haitan Island along the East China Sea are used to validate the effectiveness of the proposed method. Compared with the zero pixel percentage method and the support vector machine-based method, the experimental results demonstrate that the proposed method has better rainfall detection performance. In addition, the research on the applicability of the proposed method shows that the wave texture difference method can finish the task of rainfall detection in most marine environments.

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“…According to the latest report of Allianz global corporate & specialty (AGCS) [1], 75-96% of collisions at sea are related to crew errors. There is therefore an urgent need to use advanced artificial intelligence technology for ocean scene parsing and auxiliary decision-making on both crewed and autonomous vessels, with a longer term goal of gradually replacing the crew in plotting course and controlling the vessel [2]. If human errors can be reduced, the probability of marine collisions will 3be greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

“…On a relative scale, the product of target width and height is less than one-tenth of the entire image; on an absolute scale, the target size is less than 32 × 32 pixels [4]. (2) Poor light, undulating waves, interference due to sea surface refraction, and wake caused by ship motion, among other factors, cause large changes in the image background. (3) Given the irregular jitter as well as the sway and the heave of the hull, the onboard surveillance video inevitably shows high frequency jitter and low frequency field-ofview (FoV) shifts.…”

Section: Introductionmentioning

confidence: 99%

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

Qiao

Liu

et al. 2021

JMSE

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The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

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Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Cited by 22 publications

References 27 publications

Anti-Collision System for Small Civil Aircraft

Anti-Collision System for Small Civil Aircraft

A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

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