Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

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Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Thomas Lombaerts / Kimberlee H. Shish / Gordon Keller / Vahram Stepanyan / Nicholas Cramer / Corey Ippolito

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

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Title :

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Contributors:

Thomas Lombaerts (author) / Kimberlee H. Shish (author) / Gordon Keller (author) / Vahram Stepanyan (author) / Nicholas Cramer (author) / Corey Ippolito (author)

Conference:

AIAA SciTech Forum ; 2022 ; San Diego, US

Type of media :

Conference paper

Type of material :

No indication

Language :

English

Keywords :

kalman filter , autonomous operations , object tracking , sensor fusion , Aircraft Stability And Control , urban air mobility