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- W4283214656 abstract "Aviation safety researchers and regulators often have access to the data generated by the air traffic control and air traffic management functions of the National Airspace System. These data systems, however, are designed to provide critical position and velocity information. Unfortunately, the available data does not include information about the situation on the flight deck, where insight is needed to develop policy, design flight procedures, and collaborate with stakeholders. To fill some of that gap, the Federal Aviation Administration maintains two level-D qualified full-flight simulators dedicated to research and testing. In addition to generating direct experimental outputs, the flight simulation recordings are a rich dataset containing flight deck inputs, aircraft state, as well as position and velocity. This paper reviews the development of statistical learning models that estimate select aircraft state and flight control parameters from ADS-B surveillance data." @default.
- W4283214656 created "2022-06-22" @default.
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- W4283214656 date "2022-06-20" @default.
- W4283214656 modified "2023-10-14" @default.
- W4283214656 title "Estimating Aircraft State from Surveillance Data Using Statistical Learning" @default.
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- W4283214656 doi "https://doi.org/10.2514/6.2022-3424" @default.
- W4283214656 hasPublicationYear "2022" @default.
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