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The performance of a particle filter (PF) in nonlinear and non-Gaussianenvironments is often affected by particle degeneracy and impoverishmentproblems. In this paper, these two problems are re-assessed using the conceptsof importance region (IR) selection and particle density (PD), where IRdescribes the distribution region of particles, and PD describes the density ofparticles in IR. Based on these two factors, a support vector regression PF(SVRPF) is proposed to overcome the problems from nonlinear and non-Gaussianenvironments, especially in regard to narrow observation noise. Furthermore,the consistency of the SVRPF and Bayes' filtering is demonstrated. A numericalsimulation shows that the performance of the SVRPF is more stable than otherfilter algorithms. Provided that other conditions are the same, when theobservation noise variance is 0.1 and 5, the root-mean-square errors of theSVRPF decrease by 0.5 and 0.03, respectively, compared with that of a generalPF.

SVRPF: An Improved Particle Filter for a Nonlinear/non-Gaussian Environment