Gaussian Convolution Filter and its Application to Tracking

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

Abstract

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.

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