Multiple Model Method for Aircraft Conflict Detection and Resolution in Intent and Weather Uncertainty

This paper presents a novel multiple model (MM) method for aircraft conflict detection and resolution (CDR) in intent and weather uncertainty for the next generation air transportation system. It is based on probabilistic MM aircraft trajectory prediction. Conflict detection is performed through the...

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Vydané v:	IEEE transactions on aerospace and electronic systems Ročník 55; číslo 2; s. 1004 - 1020
Hlavní autori:	Jilkov, Vesselin P., Ledet, Jeffrey H., Li, X. Rong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Aerospace electronics Air traffic control Air transportation Aircraft Aircraft detection Algorithms Atmospheric modeling Collision avoidance Computer simulation Computing time conflict detection and resolution (CDR) Conflict resolution Evasive actions list Viterbi algorithm (LVA) Maneuvers Mathematical models model predictive control (MPC) multiple model (MM) NextGen Personal computers Predictive control Predictive models Probabilistic logic probability of conflict Search algorithms sense-and-avoid Statistical analysis Stochastic processes Trajectory Trajectory optimization trajectory prediction Transportation systems Uncertainty unmanned aerial vehicle (UAV) Unmanned aerial vehicles Viterbi algorithm detectors
ISSN:	0018-9251, 1557-9603
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Abstract	This paper presents a novel multiple model (MM) method for aircraft conflict detection and resolution (CDR) in intent and weather uncertainty for the next generation air transportation system. It is based on probabilistic MM aircraft trajectory prediction. Conflict detection is performed through the use of predicted probability of conflict (PC). An improved algorithm for estimating PC is proposed and compared with a commonly used method. Conflict resolution (CR) is formulated as a stochastic model predictive control (MPC) problem subject to a constraint on the predicted PC to guarantee safety of the optimized CR trajectories. The cost function to be minimized is tailored to the CR problem and includes the cost of performing evasive maneuvers as well as the average cost incurred by extra travel distance to the destination due to the maneuvers. An efficient algorithm for finding the optimal path (maneuver sequence), with respect to the maneuver cost, is proposed and extended to an overall CDR optimization algorithm for the total cost, in order to produce optimized conflict-free CR trajectories. The proposed search algorithm is essentially a generalization of an optimal list Viterbi algorithm (for unconstrained minimization through a trellis) to the constrained case of our MPC-based CDR problem. The capability and computational efficiency of the proposed MM-CDR method (including PC prediction, optimal search, and overall CDR) are evaluated via comprehensive simulation of a large variety of unmanned aerial vehicle "sense-and-avoid" encounter scenarios, and are compared with existing methods in the literature.
AbstractList	This paper presents a novel multiple model (MM) method for aircraft conflict detection and resolution (CDR) in intent and weather uncertainty for the next generation air transportation system. It is based on probabilistic MM aircraft trajectory prediction. Conflict detection is performed through the use of predicted probability of conflict (PC). An improved algorithm for estimating PC is proposed and compared with a commonly used method. Conflict resolution (CR) is formulated as a stochastic model predictive control (MPC) problem subject to a constraint on the predicted PC to guarantee safety of the optimized CR trajectories. The cost function to be minimized is tailored to the CR problem and includes the cost of performing evasive maneuvers as well as the average cost incurred by extra travel distance to the destination due to the maneuvers. An efficient algorithm for finding the optimal path (maneuver sequence), with respect to the maneuver cost, is proposed and extended to an overall CDR optimization algorithm for the total cost, in order to produce optimized conflict-free CR trajectories. The proposed search algorithm is essentially a generalization of an optimal list Viterbi algorithm (for unconstrained minimization through a trellis) to the constrained case of our MPC-based CDR problem. The capability and computational efficiency of the proposed MM-CDR method (including PC prediction, optimal search, and overall CDR) are evaluated via comprehensive simulation of a large variety of unmanned aerial vehicle "sense-and-avoid" encounter scenarios, and are compared with existing methods in the literature.
Author	Li, X. Rong Ledet, Jeffrey H. Jilkov, Vesselin P.
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SubjectTerms	Aerospace electronics Air traffic control Air transportation Aircraft Aircraft detection Algorithms Atmospheric modeling Collision avoidance Computer simulation Computing time conflict detection and resolution (CDR) Conflict resolution Evasive actions list Viterbi algorithm (LVA) Maneuvers Mathematical models model predictive control (MPC) multiple model (MM) NextGen Personal computers Predictive control Predictive models Probabilistic logic probability of conflict Search algorithms sense-and-avoid Statistical analysis Stochastic processes Trajectory Trajectory optimization trajectory prediction Transportation systems Uncertainty unmanned aerial vehicle (UAV) Unmanned aerial vehicles Viterbi algorithm detectors
Title	Multiple Model Method for Aircraft Conflict Detection and Resolution in Intent and Weather Uncertainty
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