Improved estimation of conflict probability for aircraft collision avoidance

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.

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