Gaussian Convolution Filter and its Application to Tracking

DOI: 10.4236/wsn.2009.12014   PDF   HTML     5,868 Downloads   9,898 Views   Citations


A new recursive algorithm, called the Gaussian convolution filter (GCF), is proposed for nonlinear dynamic state space models. Based on the convolution filter (CF) and similar to the Gaussian filters, the GCF ap-proximates the posterior density of the states by Gaussian distribution. The analytical results show the ability to deal with complex observation model and small observation noise of the GCF over the Gaussian particle filter (GPF) and the lower complexity, more amenable for parallel implementation than the CF. The Simula-tion in the Tracking domain demonstrates the good performance of the GCF.

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Q. LIN, J. YIN, J. ZHANG and B. HU, "Gaussian Convolution Filter and its Application to Tracking," Wireless Sensor Network, Vol. 1 No. 2, 2009, pp. 90-94. doi: 10.4236/wsn.2009.12014.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME–Journal of Basic Engineering, Vol. 82-D, pp. 35–45, 1960.
[2] M. K. Steven, “Fundamentals of statistical signal process-ing,” PTR Prentice-Hall, Englewood Cliffs, N.J., 1993.
[3] N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state esti-mation,” IEE Proceedings-F, Vol. 140, No. 2, pp. 107–113, 1993.
[4] A. Doucet, F. Nando, and N. J. Gordon, “Sequential Monte Carlo in practice,” Springer, New York, 2001.
[5] T. Schon, F. Gustafsson, and P. J. Nordlund, “Marginal-ized particle filters for mixed linear/nonlinear state-space models,” IEEE Transactions on Signal Processing 2005, Vol. 53, No. 7, pp. 2279–2289.
[6] J. J. Simon and K. U. Jeffrey, “Unscented filtering and nonlinear estimation,” Proceedings of the IEEE, Vol. 92, No. 3, pp. 401–422, 2004.
[7] J. H. Kotecha and P. M. Djuric, “Gaussian particle filter-ing,” IEEE Transactions on Signal Processing, Vol. 51, No. 10, pp. 2592–2601, 2003.
[8] V. Rossi and J.-P. Vila, “Nonlinear filter in discreet time: A particle convolution approach,” Biostatic Group of Monetepellier, Technical Report 04-03, 2004.
[9] F. Mustiere, M. Bolic, and M. Bouchard, “A modified Rao-blackwellised particle filter[C],” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2006, Toulouse, France, Vol. 3, pp. 21–24, May 2006.

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