A comparative study on Sigma-Point Kalman filters for trajectory estimation of hybrid aerial-aquatic vehicles

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dc.creatorRômulo T. S. da Rosa
dc.creatorPaulo J. D. O. Evald
dc.creatorPaulo Lellis Jorge Drews-jr
dc.creatorArmando Alves Neto
dc.creatorAlexandre de Campos Horn
dc.creatorRodrigo Zelir Azzolin
dc.creatorSilvia S. C. Botelho
dc.date.accessioned2025-04-14T14:03:53Z
dc.date.accessioned2025-09-09T00:42:01Z
dc.date.available2025-04-14T14:03:53Z
dc.date.issued2018
dc.identifier.doi10.1109/IROS.2018.8593556
dc.identifier.urihttps://hdl.handle.net/1843/81531
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
dc.rightsAcesso Restrito
dc.subjectAnálise de séries temporais
dc.subjectKalman, Filtragem de
dc.subjectSound analyzers
dc.subject.otherAerial robotics
dc.subject.otherUnderwater robotics
dc.subject.otherKalman filters , Trajectory tracking , State estimation , Vehicle dynamics , Robot sensing systems
dc.subject.otherKalman Filter , Root Mean Square Error , Mean Square Error , High-dimensional , Dynamic Model , Monte Carlo Simulation , Unmanned Aerial Vehicles , Inertial Measurement Unit , Nonlinear Algorithm , Nonlinear Estimation , Average Execution Time , Unscented Kalman Filter , Nonlinear State , Probability Density , Center Of Mass , Nonlinear Function , Nonlinear Systems , Morphine , Angular Velocity , Global Positioning System , State Transition Function , Extended Kalman Filter , Autonomous Underwater Vehicles , High-order Systems , Trajectory Tracking Problem , Velocity Vector , Undersea , Observation Vector , Group Of Equations , Position Error
dc.titleA comparative study on Sigma-Point Kalman filters for trajectory estimation of hybrid aerial-aquatic vehicles
dc.typeArtigo de evento
local.citation.spage7460
local.description.resumoIn this paper, a study on nonlinear state estimation methods for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) is presented. Based on a detailed dynamic model simulation, we analyse and elect the best nonlinear algorithm among those presented in the state-of-the-art literature addressing local derivative-free nonlinear Kalman Filters (KFs): the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF) and the Transformed Unscented Kalman Filter (TUKF). Here, these three nonlinear probabilistic estimators were compared in terms of the Root Mean Square Error (RMSE) and the average execution time over Monte Carlo simulations. We simulated real-world conditions for our in-production HUAUV prototype using Inertial Measurement Unit (IMU) data and state augmentation for sensor data filtering and trajectory estimation. We have concluded that the CKF proved to be the most interesting KF to low-cost on-board applications for high dimensional state spaces.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/8593556

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