A SVM Approach of Aircraft Conflict Detection in Free Flight

Xurui, Jiang; Wen, Xiaodong; Wu, Minchen; Wang, Zekun; Qiu, Xi

doi:10.1155/2018/7964641

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…According to Pham et al [14], it is essential for these models that the information is noise-free and accurate positioning, which is not possible because trajectory information comes from the approximate combined results of different surveillance radars. Recent research has thus focused rather on machine learning [1,5,9,14,20].…”

Section: Related Workmentioning

confidence: 99%

“…Many methods were proposed in which all the flights are assumed to be allowed for free routing or free-flight and that the planes share their information from the onboard system [1,2,9]. In this case, the pilots of all the aircraft involved in the conflict can resolve the conflict by communicating within themselves without the help of ATCO.…”

Section: Related Workmentioning

confidence: 99%

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Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image

Rahman

Lapasset

Mothe

2022

Intelligent Systems Design and Applications

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A situation between several moving aircraft is a conflict when their position is less than the internationally specified distance. To solve aircraft conflicts, air traffic controllers consider many parameters including the positioning coordinate, speed, direction, weather, etc. of the involved aircraft. This is a complex task, specifically considering the increase of the traffic. Assisting systems could help controllers in their tasks. Most conflict resolution models are based on trajectory data of a fixed number of input aircraft. Under this constraint, it is possible to resolve conflicts using machine learning models, including convolutional neuron network models. Such models cannot resolve conflicts that imply a variable number of aircraft because the input size of the model is fixed. To solve this challenge, we transformed the trajectory data into images which size does not depend on the number of planes. We developed a multi-label conflict resolution model that we named ACRnet, based on a convolutional neural network to classify the obtained images. ACRnet model achieves an accuracy of 99.16% on the training data and of 98.97% on the test data set for two aircraft. For both two and three aircraft, the accuracy is 99.05% (resp. 98.96%) on the training (resp. test) data set.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image

Rahman

Lapasset

Mothe

2022

Intelligent Systems Design and Applications

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“…Wang Xinglong et al [4][5][6] constructed the air traffic network based on the relationship between airports, airways and sectors, and systematically analyzed its destructiveness, toughness and resilience, etc. Wen et al [7,8] built the flight state network based on the position conflict relationship between aircraft as the basis of the connected edges between nodes, and analyzed the airspace complexity by node deletion, SVM, and other methods. The air traffic network constructed by the above scholars has certain advantages in analyzing the relationship between aircraft and the complexity of airspace.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Flight Conflict Networks Based on Degree Correlation

Wen

Lin

et al. 2022

IEEE Access

Self Cite

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Analyzing the composition and robustness of flight conflicts is beneficial to find the vulnerable sources of conflicts and dissipate flight conflicts, and effectively improve the safety and operational efficiency of air traffic. Therefore, this paper proposes a flight conflict network robustness evaluation method based on degree correlation. Firstly, a flight conflict network is constructed by using the velocity obstacle method, and it is classified into three types of assortative, disassortative and neutral conflict networks according to the degree correlation of the network. Several common robustness metrics in complex networks are explored, and a comprehensive robustness metric is proposed. The relationship between degree correlation and robustness of conflict networks under different node removal methods is analyzed by simulation experiments. The results show that the false alarm rate of the flight conflict network is decreased by 37.5% compared with the traditional state network. The degree correlation of the flight conflict network is closely related to the robustness. The assortative and disassortative conflict networks demonstrate stronger vulnerability with degree and betweenness removal, respectively, and the stronger the correlation the more obvious the vulnerability. The robustness of the neutral conflict network does not differ significantly under the two removal methods. Compared with random deletion, target removal can significantly decrease the network robustness.INDEX TERMS Flight conflict; air traffic; complex networks; degree correlation; robustness

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“…In brief, traditional flight conflict detection is based on assumptions, such as a limited flight angle, constant speed, and no exposure to environmental impacts [16,17], but errors caused by uncertainties in navigation or positioning, pilot operations and spatiotemporal environmental variations on the underlying surface, can directly affect the conflict detection accuracy. Deterministic methods use coordinates to solve and determine whether conflicts exist between aircrafts; traversing multivariate approaches involves a high algorithmic complexity and extensive computation; and probabilistic methods calculate flight probabilities using a complicated process.…”

Section: Introductionmentioning

confidence: 99%

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

Miao

Cheng

Zhai

et al. 2019

IJGI

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Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

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A SVM Approach of Aircraft Conflict Detection in Free Flight

Cited by 7 publications

References 31 publications

Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image

Aircraft Conflict Resolution Using Convolutional Neural Network on Trajectory Image

Robustness Analysis of Flight Conflict Networks Based on Degree Correlation

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

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