Weighted-interacting particle-based nonlinear filters.

  • Author(s) / Creator(s)
  • Particle-based nonlinear filters have proven to be effective and versatile methods for computing approximations to difficult filtering problems. We introduce a novel hybrid particle method, thought to possess an excellent compromise between the unadaptive nature of the weighted particle methods and the overly random resampling in classical interactive particle methods, and compare this new method to our previously introduced refining branching particle filter. Our experiments involve various fixed numbers of particles and compare computational efficiency of our new method to the incumbent. The hybrid method is demonstrated to outperform two previous particle filters on our simulated test problems. To highlight the flexibility of particle filters, we choose to test our methods on a rectangularly-constrained Markov signal that does not satisfy a typical stochastic equation but rather a Skorohod, local time formulation. Whereas normal diffusive behavior occurs in the interior of the rectangular domain, immediate reflections are enforced at the boundary. The test problems involve a fish signal with boundary reflections and is motivated by the fish farming industry.

  • Date created
    2002
  • Subjects / Keywords
  • Type of Item
    Conference Paper
  • DOI
    https://doi.org/10.7939/R3FT8DR9C
  • License
    Copyright 2002 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
  • Language
  • Citation for previous publication
    • D.J. Ballantyne, S. Kim, and M.A. Kouritzin, "Weighted-interacting particle-based nonlinear filters" in Signal Processing, Sensor Fusion, and Target Recognition XI, Ivan Kadar, Editor, 2002 Proceedings of SPIE 4729, 236-247. doi:10.1117/12.477609