Sensor Fusion with Square-Root Cubature Information Filtering

DOI: 10.4236/ica.2013.41002   PDF   HTML   XML   4,308 Downloads   6,511 Views   Citations

Abstract

This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.

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I. Arasaratnam, "Sensor Fusion with Square-Root Cubature Information Filtering," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 11-17. doi: 10.4236/ica.2013.41002.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Y. Bar Shalom, X. R. Li and T. Kirubarajan, “Estimation with Applications to Tracking and Navigation,” Wiley & Sons, New York, 2001. doi:10.1002/0471221279
[2] Y. Bar-Shalom and X. R. Li, “Multitarget-Multisensor Tracking: Principles and Techniques,” YBS, Storrs, 1995.
[3] Y. Zhu, Z. You, J. Zhao, K. Zhang and X. Li, “The Optimality for the Distributed Kalman Filtering Fusion,” Automatica, Vol. 37, No. 9, 2001, pp. 1489-1493. doi:10.1016/S0005-1098(01)00074-7
[4] T. Vercauteren and X. Wang, “Decentralized Sigma-Point Information Filters for Target Tracking in Collaborative Sensor Networks,” IEEE Transactions on Signal Processing, Vol. 53, No. 8, 2005, pp. 2997-3009. doi:10.1109/TSP.2005.851106
[5] Y. Kim, J. Lee, H. Do, B. Kim, T. Tanikawa, K. Ohba, G. Lee and J. Yun, “Unscented Information Filtering Method for Reducing Multiple Sensor Registration Error,” IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, 20-22 August 2008, pp. 326-331. doi:10.1109/MFI.2008.4648086
[6] I. Arasaratnam and S. Haykin, “Cubature Kalman Filters,” IEEE Transactions on Automatic Control, Vol. 54, No. 6, 2009, pp. 1254-1269.
[7] I. Arasaratnam, S. Haykin and T. R. Hurd, “Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations,” IEEE Transactions on Signal Processing, Vol. 58, No. 10, 2010, pp. 4977-4993.
[8] G. H. Golub and C. F. Van Loan, “Matrix Computations,” MD John Hopkins University Press, Baltimore, 1996.

  
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