Jeffrey H. Ledet

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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(More)
This paper addresses the air traffic control problem of conflict detection and resolution (CDR) under intent uncertainty, in a multiple model (MM) trajectory information processing framework. The conflict detection is based on a predicted probability of conflict. The problem of conflict resolution (CR) is formulated as one of a chance-constrained model(More)
This paper presents a follow-up and improvement of our previous work on conflict detection and resolution (CDR) for unmanned aircraft “sense-and-avoid” (SA) applications. More specifically, we propose an extension of our previous model predictive control formulation and algorithm that takes into account costs incurred by possible deviation(More)
This paper proposes a new approach for constrained multiple model (MM) maximum a posteriori (MAP) estimation through the expectation-maximization (EM) method by using our previously developed constrained sequential list Viterbi algorithm (CSLVA). The approach is general and applicable for any type of constraints provided they are verifiable. Specific(More)
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