Multiple Sensor Fusion using Adaptive Divided Difference Information Filter

Aritro Dey, Smita Sadhu, Tapan Kumar Ghoshal

2015

Abstract

This paper addresses the problem of multiple sensor fusion in situations where the system dynamics suffers from unknown parameter variation. An adaptive nonlinear information filter has been proposed for such multi sensor estimation problems where the process noise covariance becomes unknown as a consequence of unknown parameter variation. The proposed filter, based on the Divided Difference interpolation formula, ensures satisfactory estimation performance by online adaptation of the unknown process noise covariance and makes sensor fusion successful. Efficacy of the proposed filter is demonstrated with the help of a tracking problem in a sensor fusion configuration. Results from Monte Carlo simulation indicate that though the process noise covariance is unknown, the performance of the proposed filter is demonstrably superior to its non adaptive version in the context of joint estimation of parameter and states.

References

  1. T. Vercauteren, and X. Wang., 2005. Decentralized sigmapoint information filters for target tracking in collaborative sensor networks. In IEEE Trans. Signal Processing, 53 (8), 2997-3009.
  2. D. J. Lee, 2008. Nonlinear estimation and multiple sensor fusion using unscented information filtering. In IEEE Signal Processing Letters, 15, 861-864.
  3. B. Jia, M. Xin, K. Pham, E. Blasch, & G. Chen, 2013. Multiple sensor estimation using a high-degree cubature information filter. In conference SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 87390T-87390T.
  4. G. Liu, F. Worgotter and I. Markelic, 2011. Nonlinear estimation using central difference information filter. In IEEE Workshop Statistical Signal Processing. 593- 596.
  5. Q. Ge, Daxing Xu, and Chenglin Wen, 2014. Cubature information filters with correlated noises and their applications in decentralized fusion. In Journal of Signal Processing, 94, 434-444.
  6. B. D. O. Anderson and J.B. Moore, 1979. Optimal Filtering, Prentice Hall, Englewood Cliffs, 1st edition.
  7. M. Nørgaard, N. K. Poulsen, and O. Ravn, 2000. New developments in state estimation for nonlinear systems. In Automatica, 36 (11), 1627-1638.
  8. P. S. Maybeck, 1982. Stochastic models, estimation, and control, Academic Press, New York, 1st edition.
  9. A. H. Mohamed and K. P. Schwarz, 1999.Adaptive Kalman filtering for INS/GPS. In Journal of geodesy, vol. 73 (4), 193-203.
  10. M. Das, S. Sadhu, T. K. Ghoshal, 2013. An Adaptive Sigma Point Filter for Nonlinear Filtering Problems. In International Journal of Electrical, Electronics and Computer Engineering, 2 (2), 13-19.
  11. C. Hajiyev, H. E. Soken, 2014. Robust adaptive unscented Kalman filter for attitude estimation of pico satellites. In International Journal of Adaptive Control and Signal Processing, 28 (2), 107-120.
  12. D. J. Lee, 2005. Nonlinear Bayesian filtering with applications to estimation and navigation,. PhD thesis, Texas A&M University.
  13. H. Soken, S. Sakai, 2015. Adaptive Tuning of the UKF for Satellite Attitude Estimation. In Journal of Aerospace Engineering, 28 (3), DOI: 10.1061/(ASCE)AS.1943- 5525.0000412.
  14. C. D. Karlgaard, H. Schaub, 2011. Adaptive nonlinear Huber-based navigation for rendezvous in elliptical orbit, In Journal of Guidance, Control, and Dynamics, 34 (2), pp. 388-402.
  15. A, Dey, S. Sadhu, T.K. Ghoshal, 2014. Adaptive Gauss Hermite Filter for Parameter Varying Nonlinear Systems. In International Conference on Signal Processing and Communication, 1-5, DOI: 10.1109/SPCOM.2014.6983948.
Download


Paper Citation


in Harvard Style

Dey A., Sadhu S. and Ghoshal T. (2015). Multiple Sensor Fusion using Adaptive Divided Difference Information Filter . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 398-406. DOI: 10.5220/0005537303980406


in Bibtex Style

@conference{icinco15,
author={Aritro Dey and Smita Sadhu and Tapan Kumar Ghoshal},
title={Multiple Sensor Fusion using Adaptive Divided Difference Information Filter},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={398-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005537303980406},
isbn={978-989-758-122-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Multiple Sensor Fusion using Adaptive Divided Difference Information Filter
SN - 978-989-758-122-9
AU - Dey A.
AU - Sadhu S.
AU - Ghoshal T.
PY - 2015
SP - 398
EP - 406
DO - 10.5220/0005537303980406