scholarly journals Satellite Inertia Parameters Estimation Based on Extended Kalman Filter

2019 ◽  
Author(s):  
Abdellatif Bellar ◽  
Mohammed Arezki Si Mohammed
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Critical Role ◽  
Low Earth Orbit ◽  
Parameters Estimation ◽  
Moment Of Inertia ◽  
Satellite Mission ◽  
Inertia Matrix ◽  
Inertia Parameters ◽  
The Moment

The moment of inertia parameters play a critical role in assuring the spacecraft mission throughout its lifetime. However, determination of the moment of inertia is a key challenge in operating satellites. During satellite mission, those parameters can change in orbit for many reasons such as sloshing, fuel consumption, etc. Therefore, the inertia matrix should be estimated in orbit to enhance the attitude estimation and control accuracy. This paper investigates the use of gyroscope to estimate the attitude rate and inertia matrix for low earth orbit satellite via extended Kalman filter. Simulation results show the effectiveness and advantages of the proposed algorithm in estimating these parameters without knowing the nominal inertia. The robustness of the proposed algorithm has been validated using the Monte-Carlo method. The obtained results demonstrate that the accuracy of the estimated inertia and angular velocity parameters is satisfactory for satellite with coarse accuracy mission requirements. The proposed method can be used for different types of satellites.

Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions

2018 ◽  
Vol 233 (4) ◽  
pp. 1250-1269 ◽  
Author(s):  
Jianping Yuan ◽  
Xianghao Hou ◽  
Chong Sun ◽  
Yu Cheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Formation Flying ◽  
Fault Tolerant ◽  
Estimation Algorithm ◽  
Parameters Estimation ◽  
Small Satellites ◽  
Dual Vector ◽  
Inertial Parameters ◽  
Space Target

Estimating the parameters of an unknown free-floating tumbling spacecraft is an essential task for the on-orbit servicing missions. This paper proposes a dual vector quaternion based fault-tolerant pose and inertial parameters estimation algorithm of an uncooperative space target using two formation flying small satellites. Firstly, by utilizing the dual vector quaternions to model the kinematics and dynamics of the system, not only the representation of the model is concise and compacted, but also the translational and rotational coupled effects are considered. By using this modeling technique along with the measurements from the on-board vision-based sensors, a dual vector quaternion based extended Kalman filter for each of the two small satellites is designed. Secondly, both of the estimations from each small satellite will be used as inputs of the fault-tolerant algorithm. This algorithm is based on the fault-tolerant federal extended Kalman filter strategy to overcome the estimation errors caused by the faulty measurements, the unknown space environment and the computing errors by setting the appropriate ratios of the two estimations from the first step dual vector quaternions extended Kalman filter. Together with the first and second steps, a novel fault-tolerant dual vector quaternions federal extended Kalman filter using two formation flying small satellites is proposed by this paper to estimate the pose and inertial parameters of a free-floating tumbling space target. By utilizing the estimation algorithm, a good prior knowledge of the unknown space target can be achieved. Finally, the proposed dual vector quaternion federal extended Kalman filter is validated by mathematical simulations to show its robust performances.

INERTIA PARAMETERS ESTIMATION OF PLANAR OBJECT ON ROBOT PUSHING OPERATION

2009 ◽  
Vol 06 (04) ◽  
pp. 239-247 ◽  
Author(s):  
YONG YU ◽  
TETSU ARIMA ◽  
SHOWZOW TSUJIO
Keyword(s):  
Experimental Verification ◽  
Center Of Mass ◽  
Point Contact ◽  
Parameters Estimation ◽  
Moment Of Inertia ◽  
Inertia Parameters ◽  
Angular Velocities ◽  
Unknown Object

This paper proposes a technique that can estimate the inertia parameters of a graspless unknown object, which is pushed by robot fingers. Using the fingertip different accelerations (or angular accelerations), velocities (or angular velocities) and forces information measured in pushing operations, the algorithms to estimate the object mass (or moment of inertia) are described. Then, a line called C.M. Line, is defined in this paper. The line contains the center of mass and is between two fingertips which are in point-contact with an object side. By using two or more orientation-different C.M. lines, an algorithm to estimate the center of mass of the object is given. Lastly, experimental verification on the proposed approach is performed and its results are outlined.

The accurate continuous-discrete extended Kalman filter for continuous-time stochastic systems

2015 ◽  
Vol 30 (4) ◽  
Author(s):  
Gennady Yu. Kulikov ◽  
Maria V. Kulikova
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Nonlinear Filtering ◽  
Stochastic Systems ◽  
Total Error ◽  
Stochastic Dynamic ◽  
Extended Kalman Filtering ◽  
New Approach ◽  
Error Covariance ◽  
The Moment

AbstractThis paper elaborates a new approach to nonlinear filtering based on an accurate implementation of the continuous-discrete extended Kalman filter. It implies that the moment differential equations for calculating the predicted state mean of stochastic dynamic system and the corresponding error covariance matrix are solved accurately, i.e. with negligible error. The latter allows the total error of the extended Kalman filter to be reduced significantly and results in a new Accurate Continuous-Discrete Extended Kalman Filtering method. The developed technique is compared theoretically and numerically with other implementations of the extended Kalman filter to conform its outstanding performance on test examples.

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