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• W3118697435 abstract "Autonomy proliferates air and space traffic management with the National Aeronautics and Space Administration (NASA) initiative on Unmanned Aircraft System Traffic Management (UTM). New endeavors such as electric vertical take-off and landing (eVTOL) and COVID19 are challenging every aspect of the NextGEN rollout. Hence, to develop a safe and secure UTM, there is need for knowledge management. Knowledge management comes from awareness about the environment and awareness is based on assessment. The information fusion community has long developed methods for data fusion (e.g., statistical analysis), sensor fusion (e.g., navigation and tracking), information fusion (e.g., Notice to Airman and air tracks), as well intelligence fusion (e.g., response to malicious attacks) for knowledge assessment. Each of these techniques has opportunities to enable UTM autonomy, joint all-domain command and control, and surveillance. This paper explores the various uses of fusion available to support the autonomy. For example, three types of autonomy have been proposed: autonomy at rest (e.g., flight plans, radar positions), autonomy in motion (e.g., dynamic tracking with automatic dependent surveillance-broadcast (ADS-B) and weather), as well as autonomy in use (e.g., getting the right data at the correct time). A use case is presented for UTM that utilizes data fusion from ADS-B, radar, LiDAR, and visual data to provide effective positioning in response to various cyber attacks on the ADS-B data." @default.
• W3118697435 created "2021-01-18" @default.
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• W3118697435 date "2021-01-04" @default.
• W3118697435 modified "2023-09-23" @default.
• W3118697435 title "Information Fusion as an Autonomy enabler for UAV Traffic Management" @default.
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• W3118697435 doi "https://doi.org/10.2514/6.2021-0658" @default.
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