Multicriteria Collision Risk Ranking of Aircraft by Data from an Onboard Radar Station

To simulate echoes from the earth’s surface in the low flight mode, it is necessary to reproduce reliably the delayed reflected sounding signal of the radar in real time. For this, it is necessary to be able to calculate accurately and quickly the dependence of the distance to the object being measured from the angular position of the line of sight of the radar station. Obviously, the simplest expressions for calculating the range can be obtained for a segment or a plane. In the text of the article, analytical expressions for the calculation of range for two-dimensional and three-dimensional cases are obtained. Methods of statistical physics, vector algebra, and the theory of the radar of extended objects were used. Since the calculation of the dependence of the range of the object to the target from the angular position of the line of sight is carried out on the analytical expressions found in the paper, the result obtained is accurate, and due to the relative simplicity of the expressions obtained, the calculation does not require much time.

Download Full-text

Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

Applied Sciences ◽

10.3390/app11073103 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3103

Author(s):

Kyuman Lee ◽

Daegyun Choi ◽

Donghoon Kim

Keyword(s):

Collision Avoidance ◽

Search Algorithm ◽

Unmanned Aircraft ◽

Artificial Potential Field ◽

Tree Search ◽

Local Minima ◽

Collision Risk ◽

Motion Primitives ◽

Multiple Uavs ◽

Tree Search Algorithm

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

Download Full-text

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

Journal of Navigation ◽

10.1017/s0373463321000461 ◽

2021 ◽

pp. 1-22

Author(s):

Lei Jinyu ◽

Liu Lei ◽

Chu Xiumin ◽

He Wei ◽

Liu Xinglong ◽

...

Keyword(s):

Least Squares Method ◽

Automatic Identification ◽

Identification System ◽

Collision Risk ◽

Automatic Identification System ◽

Domain Models ◽

System Data ◽

Quantification Model ◽

Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

Download Full-text

Development and experimental researches of refrigerating unit with a radar station

Journal of Physics Conference Series ◽

10.1088/1742-6596/1675/1/012109 ◽

2020 ◽

Vol 1675 ◽

pp. 012109

Author(s):

V N Beschastnyh ◽

Yu A Borisov ◽

A S Kosoi ◽

L V Nizovskiy

Keyword(s):

Radar Station ◽

Experimental Researches

Download Full-text

An Estimation of Ship Collision Risk Based on Relevance Vector Machine

Journal of Marine Science and Engineering ◽

10.3390/jmse9050538 ◽

2021 ◽

Vol 9 (5) ◽

pp. 538

Author(s):

Jinwan Park ◽

Jung-Sik Jeong

Keyword(s):

Machine Learning ◽

Decision Making ◽

Risk Estimation ◽

Good Method ◽

Relevance Vector Machine ◽

Machine Learning Algorithms ◽

Support Vector ◽

Collision Risk ◽

Estimation Model ◽

Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

Download Full-text