Publication

Linear Parameter-Varying Kalman Filter for angular velocity estimation of a reaction sphere actuator for satellite attitude control

Related publications (32)

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Model Based Navigation of Delta-Wing UAV - In-Flight Calibration and Autonomous Performance

Jan Skaloud, Gabriel François Laupré, Pasquale Longobardi

This paper presents the first practical results of a model-based approach to autonomous navigation applied to a small delta wing drone. The aerodynamic coefficients of the considered platform are unknown and need to be determined for the (vehicle dynamic) ...

2021

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint

Kamiar Aminian, Frédéric Meyer, Salil Apte, Farzin Dadashi, Vincent Gremeaux

Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estim ...

2020

The use of a Bayesian filter (e.g., Kalman filter) for the fusion of information from satellite positioning and inertial navigation is a common approach in many applications, where the knowledge of position, velocity, and attitude in space are of great int ...

EPFL2019

MATHICSE Technical Report : Efficient state/Parameter estimation in nonlinear unsteady PDEs by reduced basis ensemble Kalman filter

Alfio Quarteroni, Andrea Manzoni, Stefano Pagani

The ensemble Kalman filter is a computationally efficient technique to solve state and/or parameter estimation problems in the framework of statistical inversion when relying on a Bayesian paradigm. Unfortunately its cost may become moderately large for sy ...

MATHICSE2019

Construction of dynamically-dependent stochastic error models, 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

Jan Skaloud, Stéphane Guerrier, Philipp Clausen

Stochastic behavior of an instrument is often analyzed by constructing the Allan (or wavelet) variance signatures from an error signal. For inertial sensors, such a signature is conveniently obtained by recording data at rest. The analysis of this signal w ...

IEEE ION PLANS2018

Local-polynomial-approximation-based phase unwrapping using state space analysis

Pramod Rastogi, Rishikesh Dilip Kulkarni

A new method of phase unwrapping is proposed for the absolute phase estimation based on the first-order polynomial phase approximation within a symmetric window generated around each pixel. The accurate estimation of polynomial coefficients is performed by ...

2017

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In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming mea ...

Institute of Electrical and Electronics Engineers2017

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State estimation techniques are used for improving the quality of measured signals and for reconstructing unmeasured quantities. In chemical reaction systems, nonlinear estimators are often used to improve the quality of estimated concentrations. These non ...

Elsevier Science Bv2016

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This paper aims to assess the performance of linear state estimation (SE) processes of power systems relying on synchrophasor measurements. The performance assessment is conducted with respect to two different families of SE algorithms, i.e., static ones r ...

Institute of Electrical and Electronics Engineers2016

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