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Data-driven zonotopic approximation for n-dimensional probabilistic geofencing
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DOI：10.1016/j.ress.2023.109923.png)
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Advanced air mobility is a promising way of metropolitan air transportation. One critical concern that arises is how to ensure operational safety in high -dense, dynamic, and uncertain airspace environments in real time. To address this challenge, we seek a probabilistic geofence that bounds system states with high confidence. To identify the n -dimensional probabilistic geofence for arbitrary unknown uncertainties not limited to Gaussian ones, we present an online algorithm based on a data -driven approach of kernel density estimator. Considering the irregular shape of the probabilistic geofence, we formulate an optimization framework of integer linear programming whose solution determines a zonotope which provides a convex approximation for the probabilistic geofence. Leveraging this formulation, a heuristic algorithm is developed to find its solution efficiently without losing notable accuracy. This heuristic algorithm is tested on case studies that demonstrate it enjoys efficiency, accuracy, near -optimality, and robustness simultaneously.
Keyword:
Probabilistic geofence
Zonotopic approximation
Uncertain dynamic system
Kernel density estimator
Integer linear programming
期刊
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11
论文数:
9.0K
被引数:
4.2W
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