The inclusion of large-scale data-monitoring and data-storage systems has presented researchers with new methods and opportunities for understanding air traffic systems. Traditionally, the study of air traffic systems has required laborious real-time monitoring of human pilots and controllers for analysis that yields only qualitative descriptions of the subjects' behavior. Computer systems can rapidly automate many analysis processes on historical data sets covering weeks, months, and even years. This paper takes a step in the direction of data-driven analysis by introducing an off-line algorithm for detecting aircraft trajectory changes in heading, speed, and altitude. The algorithm recovers control signal inputs (e.g. heading changes) via sparsity-inducing norm minimization. The algorithm is able to overcome noise in position measurements to construct reliable estimates of the number of maneuvers and inter-maneuver aircraft states (e.g. ground-speed, heading, and their derivatives).
A posteriori aircraft control signal recovery via sparsity-inducing norm minimization
2012-10-01
645062 byte
Conference paper
Electronic Resource
English
A posteriori aircraft control signal recovery via sparsity-inducing norm minimization
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