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Unscented Kalman filter for spacecraft attitude estimation using modified Rodrigues parameters and real data

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Abstract

In this paper, a sigma-point Kalman filter formulation for attitude estimation is derived using the modified Rodrigues parameters and real data of attitude sensors. The unscented Kalman filter algorithm is used for attitude estimation and the gyro-based model is considered for attitude propagation. In this study, the attitude of satellite is estimated using real data supplied by gyros, Earth sensors and Sun sensors that are on board of the CBERS-2 (China Brazil Earth Resources Satellite). The results show that even with a sparse set of measures, the UKF with MRP shows results similar to those obtained when directly uses the Euler angles.

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Acknowledgments

The authors would like to thank the financial support received by FAPESP (2012/21023-6) e CNPq (303119/2010-1).

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Authors and Affiliations

  1. Lorena School of Engineering, Sao Paulo University, Estrada Municipal do Campinho, s/n, Lorena, SP, CEP: 12.602-810, Brazil

    Roberta Veloso Garcia

  2. National Institute for Space Research, Av. dos Astronautas, 1758, Jd da Granja, São José dos Campos, SP, CEP: 12.227-010, Brazil

    Nicholas de Freitas Oliveira Matos

  3. Sao Paulo University (UNESP), Av. Ariberto Pereira da Cunha, 333, Portal das Colinas, Guaratinguetá, SP, CEP: 12.516-410, Brazil

    Hélio Koiti Kuga

  4. Federal University of ABC, Av. dos Estados, 5001, Bairro Bangu, Santo André, SP, CEP: 09210-580, Brazil

    Maria Cecılia Zanardi

Authors
  1. Roberta Veloso Garcia
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  2. Nicholas de Freitas Oliveira Matos
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  3. Hélio Koiti Kuga
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  4. Maria Cecılia Zanardi
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Corresponding author

Correspondence to Roberta Veloso Garcia.

Additional information

Communicated by Dr. Elbert E. N. Macau, Dr. Antônio Fernando Bertachini de Almeida Prado and Dr. Cristiano Fiorilo de Melo.

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Garcia, R.V., de Freitas Oliveira Matos, N., Kuga, H.K. et al. Unscented Kalman filter for spacecraft attitude estimation using modified Rodrigues parameters and real data. Comp. Appl. Math. 35, 835–846 (2016). https://doi.org/10.1007/s40314-015-0269-5

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  • DOI: https://doi.org/10.1007/s40314-015-0269-5

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