Gaussian Convolution Filter and its Application to Tracking

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 approximates 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 Simulation in the Tracking domain demonstrates the good performance of the GCF.