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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.

has been cited by the following article:

• TITLE: Gaussian Convolution Filter and its Application to Tracking

  AUTHORS: Qing LIN, Jianjun YIN, Jianqiu ZHANG, Bo HU

  KEYWORDS: Signal Processing, Tracking, Nonlinear Estimation

  JOURNAL NAME: Wireless Sensor Network, Vol.1 No.2, July 13, 2009

  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.