Conflict Detection Performance of Non-Cooperative Sensing Architectures for Small UAS Sense and Avoid

Author(s):  
Roberto Opromolla ◽  
Giancarmine Fasano ◽  
Domenico Accardo
2009 ◽  
Vol 42 (8) ◽  
pp. 65-70 ◽  
Author(s):  
Fredrik Lindsten ◽  
Per-Johan Nordlund ◽  
Fredrik Gustafsson

2021 ◽  
Author(s):  
Jimmy Y. Zhong ◽  
Sim Kuan Goh ◽  
Chuan Jie Woo ◽  
Sameer Alam

Abstract With a focus on psychometric assessment, the current study investigated the extent to which spatial orientation ability (SOA), as conceptualized in the spatial cognition and navigation literature, predicted air traffic conflict detection performance in a simulated free route airspace (FRA). Within a FRA, airspace users have the flexibility to plan flights by selecting preferred routes between predefined waypoints. Despite such benefits, FRA implementation can introduce conflicts that are geometrically complex, and of which would require a high level of SOA engagement. Based on a sample of 20 young adults who have the prospect to become air traffic controllers (ATCOs), we found that response time-based performance on a newly developed computerized spatial orientation test (SOT) predicted time to loss of minimum separation (tLMS)-based performance on a conflict detection task to a moderately large extent under scenarios with high air traffic density. We explained these findings in light of similar or overlapping mental processes that were most likely activated optimally under task conditions featuring approximately equal numbers of outcome-relevant stimuli. We also discussed the potential use of the new SOT in relation to the selection of prospective ATCOs who can demonstrate high levels of conflict detection performance in FRA during training simulations.


Author(s):  
Mengting Yuan ◽  
Hongwei Shi

In the context of airspace fusion, in order to improve the safety performance of UAV and prevent the occurrence of air collision accidents, an ant colony algorithm model for UAV sense and avoid based on ADS-B monitoring technology is proposed. The model mainly consists of two parts: the deterministic conflict detection model makes the full use of ADS-B information to calculate the geometric distance from the horizontal and vertical planes to identify the conflict target, and the conflict resolution model is based on the ant colony algorithm which introduces the comprehensive heuristic function and sorting mechanism to plan the route again for achieving the collision avoidance. The simulation results show that the conflict detection model can effectively identify the possible threat targets, and the conflict resolution model is not only suitable for the typical two aircraft conflict scenarios, but also can provide a better resolution strategy for the complex multiple aircraft conflict scenarios.


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