This paper addresses the problem of conflict detection & resolution for air traffic control based on trajectory information processing. Most probabilistic methods for estimating the probability of conflict (PC) in the literature assume a Gaussian distribution of the predicted separation vector between two aircraft. In an advanced multiple model trajectory prediction framework, however, this separation vector has a Gaussian mixture distribution, and consequently, the available methods for estimating PC may lack the desired accuracy in a highly uncertain trajectory environment. This papers proposes a more accurate method for estimating PC by utilizing the information from multiple model aircraft trajectory prediction. The predicted PC for a Gaussian mixture distribution of the separation vector between two aircraft is derived and an efficient algorithm for numerical evaluation is proposed. Simulation and comparison of the proposed approach with a traditional Gaussian-based approach over a ¿sense-and-avoid¿ unmanned aircraft scenario are presented, which demonstrate improvement.
Improved estimation of conflict probability for aircraft collision avoidance
01.07.2014
8139320 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Autonomous collision avoidance based on aircraft performances estimation
IEEE | 2011
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