Paper Titles
Stability Analysis of a Light Aircraft Configuration Using Computational Fluid Dynamics
p.391
On the Evaluation of Negative Altitude Requirement for Flutter Speed Boundary of Transport Aircraft and UAV
p.397
Three Dimensional Path Planning Algorithm for Small UAVs Incorporating Existing Dynamic Soaring Heuristics
p.403
Investigation on a Flexible Tether Slackness Based on In- and Out-of Plane Libration Angles
p.411
Spacecraft Attitude and System Identification Using Marginal Reduced UKF Utilizing the Sun and Calibrated TAM Sensors
p.417
Spray Characteristics of Swirl Effervescent Injector in Rocket Application: A Review
p.423
Parametric Study of Ground-Track Based Performance Measure Requirements for the Optimization of Near Earth Twin-Spacecrafts Trajectory
p.429
Load Cell Application in Rocket Thrust Measurement System
p.437
Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping
p.442

Spacecraft Attitude and System Identification Using Marginal Reduced UKF Utilizing the Sun and Calibrated TAM Sensors

Article Preview

Abstract:

This paper deals with attitude determination, parameter identification and reference sensor calibration simultaneously. A LEO satellite’s attitude, inertia tensor as well as calibration of Three-Axis-Magnetometer (TAM) are estimated during a maneuver designed to satisfy persistency of excitation condition. For this purpose, kinematic and kinetic state equations of spacecraft motion are augmented for the determination of inertia tensor and TAM calibration parameters including scale factors, misalignments and biases along three body axes. Attitude determination is a nonlinear estimation problem. Unscented Kalman Filter (UKF) as an advanced nonlinear estimation algorithm with good performance can be used to estimate satellite attitude but its computational cost is considerably larger than the widespread, low accuracy, Extended Kalman Filter (EKF). Reduced Sigma Points Filters provide good solutions and also decrease run time of UKF. However, in contrast to nonlinear problem of attitude determination, parameter identification and sensor calibration have linear dynamics. Therefore, a new Marginal UKF (MUKF) is proposed that combines the utility of Kalman Filter with Modified UKF (MMUKF). The proposed MMUKF utilizes only 14 sigma points to achieve the complete 25-dimensional state vector estimation. Additionally, a Monte Carlo simulation has demonstrated a good accuracy for concurrent estimation of attitude, inertia tensor as well as TAM calibration parameters in significantly less time with respect to sole utilization of the UKF.

You might also be interested in these eBooks
AEROTECH IV View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 225)

Pages:

417-422

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.225.417

Citation:

Cite this paper

Online since:

November 2012

Authors:

Maryam Kiani, Seid H. Pourtakdoust

Keywords:

Attitude Determination, Inertia Matrix Identification, Marginal Filter, Reduced Sigma Point Kalman Filter, Sensor Calibration, Unscented Kalman Filter (UKF)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] J. A. Wertz and A. Y. Lee, In-Flight Estimation of the Cassini Spacecraft's Inertia Tensor, Journal of spacecraft and Rockets, Vol. 39, No. 1, (2000) 153-155.

DOI: 10.2514/2.3795

Google Scholar

[2] B-E Jun, D. S. Bernstein and N. H. McClamroch, Identification of the Inertia Matrix of a Rotating Body Based on Errors-in-Variables Models, International Journal of Adaptive Control and Signal Processing, (2009).

DOI: 10.1002/acs.1112

Google Scholar

[3] Y. V. Kim, K.J. Di Filippo, A. Ng, On the Calibration of Satellite On-Board Magnetometer, The Fourth International Conference on Control and Automation (ICCA'03), (2003).

DOI: 10.1109/icca.2003.1595162

Google Scholar

[4] J. L. Crassidiss, K-L Lai, R. R. Harman, Real-Time Attitude-Independent Three-Axis Magnetometer Calibration, AIAA Journal of Guidance, Control and dynamics, Vol. 28, No. 1,(2005) 115-120.

DOI: 10.2514/1.6278

Google Scholar

[5] M. E. Pittelkau, Kalman Filtering for Spacecraft System Alignment Calibration, Journal of Guidance, Control and Dynamics, Vol. 24, No. 6, (2001) 1187-1195.

DOI: 10.2514/3.22615

Google Scholar

[6] K-L. Lai, J. L. Crassidiss,, R. R. Harman, In-Space Spacecraft Alignment Calibration Using the Unscented Filter, AIAA Guidance, Navigation and Control Conference, Austin, (2003).

DOI: 10.2514/6.2003-5563

Google Scholar

[7] E. Kim and H-C Bang, Bias Estimation of Magnetometer Using Genetic Algorithm, International Conference on Control, Automation and Systems,( 2007).

DOI: 10.1109/iccas.2007.4406907

Google Scholar

[8] A. Kutlu, Ch. Hacryev, O. Tekinalp, Attitude Determination and Rotational Motion Parameters Identification of a LEO Satellite Through Magnetometer and Sun Sensor Data, International Conference on Recent Advances in Space Technologies , (2007).

DOI: 10.1109/rast.2007.4284033

Google Scholar

[9] H. Myung, K-L Yong and H. Bang, Hybrid Estimation of Spacecraft Attitude Dynamics and Rate Sensor Alignment Parameters, International Conference on Control, Automation and Systems, (2007).

DOI: 10.1109/iccas.2007.4406904

Google Scholar

[10] M. J. Sidi, Spacecraft Dynamics and Control, Cambridge University Press, 1997.

Google Scholar

[11] D. Shuster M., A Survey of Attitude Representations, The Journal of the Astronautical Sciences, V. 41, No. 3 (1993)

Google Scholar

[12] P. H. Zipfel, Modeling and Simulation of Aerospace Vehicle Dynamics, AIAA Education Series, (2000)

Google Scholar

[13] S. J. Julier, J. K. Uhlmann, A New Extension of the Kalman Filter to Nonlinear Systems, in Proc. Of AeroSense:The 11th Int. Symposium On Aerospace/Defense Sensing, Simulation and Controls, (1997)

Google Scholar

[14] Li w.-Ch., Wei P., Xiao X-C., Novel Simplex Unscented Transform and Filter, Journal of Electronic and Technology of China, Vol. 6, N. 1, (2008)

Google Scholar

[15] C. C Finlay., S.Maus, C. D.Began, and et al, International Geomagnetic Reference field: the eleven generation, Geophys. J. Int., V. 183, (2010)

Google Scholar