D. Guo, X. Wang, and R. Chen, “New sequential monte carlo methods for nonlinear dynamic systems,” Statistics and Computing, Vol. 15, No. 2, pp. 135–147, 2005.

has been cited by the following article:

• TITLE: Particle Filtering Optimized by Swarm Intelligence Algorithm

  AUTHORS: Wei Jing, Hai Zhao, Chunhe Song, Dan Liu

  KEYWORDS: Filtering Method, Particle Filtering, Unscented Kalman Filter, Particle Swarm Optimizer

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.1, March 1, 2010

  ABSTRACT: A new filtering algorithm — PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which means that these particles will move to the region with higher weights. This process can be regarded as one-step predefined PSO process, so the proposed algo-rithm is named PSO-UPF. Although the PSO process increases the computing load of PSO-UPF, but the refined weights may make the proposed distribution more closed to the poster distribution. The proposed PSO-UPF algorithm was compared with other several filtering algorithms and the simulating results show that means and variances of PSO-UPF are lower than other filtering algorithms.