TY - GEN
T1 - Data-driven robust UAV position estimation in GPS signal-challenged environment
AU - Yi, Shenglun
AU - Jin, Xuebo
AU - Wang, Zhengjie
AU - Liu, Zhijun
AU - Zorzi, Mattia
N1 - Publisher Copyright:
© 2025 AACC.
PY - 2025
Y1 - 2025
N2 - In this paper, we consider a position estimation problem for an unmanned aerial vehicle (UAV) equipped with both proprioceptive sensors, i.e. IMU, and exteroceptive sensors, i.e. GPS and a barometer. We propose a data-driven position estimation approach based on a robust estimator which takes into account that the UAV model is affected by uncertainties and thus it belongs to an ambiguity set. We propose an approach to learn this ambiguity set from the data.
AB - In this paper, we consider a position estimation problem for an unmanned aerial vehicle (UAV) equipped with both proprioceptive sensors, i.e. IMU, and exteroceptive sensors, i.e. GPS and a barometer. We propose a data-driven position estimation approach based on a robust estimator which takes into account that the UAV model is affected by uncertainties and thus it belongs to an ambiguity set. We propose an approach to learn this ambiguity set from the data.
UR - https://www.scopus.com/pages/publications/105015684254
U2 - 10.23919/ACC63710.2025.11107768
DO - 10.23919/ACC63710.2025.11107768
M3 - Conference contribution
AN - SCOPUS:105015684254
T3 - Proceedings of the American Control Conference
SP - 491
EP - 496
BT - 2025 American Control Conference, ACC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 American Control Conference, ACC 2025
Y2 - 8 July 2025 through 10 July 2025
ER -