A Nonlinear Filtering Approach for the Attitude and Dynamic Body Acceleration Estimation Based on Inertial and Magnetic Sensors: Bio-Logging Application

Fourati, Hassen; Manamanni, Noureddine; Afilal, Lissan; Handrich, Yves

doi:10.1109/jsen.2010.2053353

Cited by 86 publications

(49 citation statements)

References 29 publications

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“…In many cases extra sensors may facilitate the solution. For example, global positioning system (GPS) was used in [75]; magnetic field sensors were utilized in [143,68], additional bearing information was required in [11]; landmark measurements were used in [190], Earth horizon sensor was utilized in [83], active vision system was employed in [24]. Like robotic systems, living creatures solve the problem of estimation gravitational verticality using limited set of sensors.…”

Section: Verticality Estimation Methodsmentioning

confidence: 99%

Modeling Verticality Estimation During Locomotion

Farkhatdinov

Michalska

Berthoz

et al. 2013

Romansy 19 – Robot Design, Dynamics and Control

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In this thesis, a nonlinear model of the vestibular system is proposed, with special reference to humans and other locomoting animals. The vestibular system is essential for stable locomotion since it provides idiothetic measurements of spatial orientation that are needed for postural control. The model was constructed from general considerations regarding the Newton-Euler dynamics governing the three-dimensional movements of bodies constrained to oscillate in non-inertial frames, such as the otoliths, which were modeled as spherical damped pendula. Two configurations were considered. The medial model considered only one inner ear located in the center of a head. The lateral model considered two inner ears located laterally with respect to the center of rotation of the head. The differences between these two models were analyzed and the importance of having two vestibular organs discussed. To this end, a nonlinear algebraic observability test was used to verify whether the reconstruction of the head orientation with respect to the gravitational vertical was possible from otoliths measurements only. It could be shown that in order for the head vertical orientation to be observable, the head had to be stabilized during locomotion. Moreover, it was shown that the gravitoinertial ambiguity inherent to inertial idiothetic sensing could be resolved if the head was horizontally stabilized. These results were applied to solve the head vertical orientation estimation problem in the linearized case (Luenberger observer, Kalman filter) as well as in the nonlinear case (Extended Kalman filter, Newton method based observation). These observers were designed and tested in simulations. The simulations indicated that the estimation errors were smaller and the observers converged faster when head was stabilized during locomotion, leading to a nonlinear, combined observation-control system that could be stabilized with respect to the gravitational vertical based on no other information than the prior knowledge of the Newton-Euler dynamics. The results were further tested with a specifically designed experimental setup that comprised an actuated gimbal mechanism to represent the head-neck articulation and a liquid-based inclinometer that represented the otoliths organs. The findings derived from this research would be helpful for analyzing spatial perception in humans and animals, and for improving the perceptual capabilities of robotic systems, such as humanoid robots, rough terrain vehicles, or free-moving drones.Keywords: vestibular system, head-neck system, nonlinear system, observation, estimation, verticality, spatial perception, locomotion, humanoid robot. 3 RésuméDans cette thèse, nous proposons un modèle non-linéaire du système vestibulaire, en particulier chez l'humain et autres animaux mobiles. Le système vestibulaire est essentiel pour une locomotion stable dans la mesure où il fournit des mesures idiothétiques d'orientation spatiale nécessairesà la stabilité posturale. Le développement du modèle est basé sur l...

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Section: Verticality Estimation Methodsmentioning

confidence: 99%

Modeling Verticality Estimation During Locomotion

Farkhatdinov

Michalska

Berthoz

et al. 2013

Romansy 19 – Robot Design, Dynamics and Control

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“…This family of filters are computationally efficient and are asymptotically stable. Moreover DCM based filter formulation is applicable to all orientation and avoids singularities and approximation errors [10][11][12] . The attitude kinematics of the true system using direction cosine matrix (DCM) is represented by DCM is written in terms of rotation matrix that describes the orientation of the body coordinates frame b with respect to the inertial/navigation frame n. Rotation matrix n b C can be expressed as cos cos -cos sin +sin sin cos sin sin +cos sin cos cos sin cos cos +sin sin sin -sin cos +cos sin sin -sin sin cos cos cos…”

Section: Nonlinear Complimentary Filtermentioning

confidence: 99%

Multiplicative Error State Kalman Filter vs Nonlinear Complimentary Filter for a High Performance Aircraft Attitude Estimation

Kamali

Jain

2016

Def. Sc. Jl.

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Modern control law designs increasingly use aircraft attitude information to improve aircraft manoeuverability. Attitude information allows for gravity term compensations in the longitudinal as well as lateral directional control laws of a typical fighter aircraft. Methodologies and comparisons of multiplicative error state Kalman filter (MEKF) and nonlinear complimentary filter for estimation of attitudes of a high performance aircraft using its onboard autonomous sensors is presented. Shows a problem in pitch angle estimation beyond ± 80 deg in the MEKF and a solution is proposed for the same for the first time. Also presents novel aiding sensor modelling for the implementation of attitude heading reference system for this class of aircraft for the first time. The filter formulations are evaluated using full range manuoevering real flight test data.

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“…Dead reckoning suffers the most from the accumulation of errors [10]. Commonly found in current systems is another approach using extended stochastic linear estimation techniques [6,7,8]. However, the Gaussian noise assumptions of such filters do not apply well, and frequent re initialization is required.…”

Section: Complementary Trackermentioning

confidence: 99%

Action Trajectory Reconstruction for Controlling of Vehicle Using Sensors

Prasanna¹

2013

IOSRJECE

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A Nonlinear Filtering Approach for the Attitude and Dynamic Body Acceleration Estimation Based on Inertial and Magnetic Sensors: Bio-Logging Application

Cited by 86 publications

References 29 publications

Modeling Verticality Estimation During Locomotion

Modeling Verticality Estimation During Locomotion

Multiplicative Error State Kalman Filter vs Nonlinear Complimentary Filter for a High Performance Aircraft Attitude Estimation

Action Trajectory Reconstruction for Controlling of Vehicle Using Sensors

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