Non-recursive and recursive methods for parameter estimation in filtering problems.

• Author(s) / Creator(s)

  • Kouritzin, Michael
  • Ma, Xinjian
  • Long, Hongwei
  • Sun, Wei

• Nonlinear filtering is an important and effective tool for handling estimation of signals when observations are incomplete, distorted, and corrupted. Quite often in real world applications, the signals to be estimated contain unknown parameters which need to be determined. Herein, we develop and analyze non-recursive and recursive methods, which can deal with combined state and parameter estimation for nonlinear partially-observed stochastic systems. For the non-recursive method, we obtain the unknown parameters through solving a system of non-singular finite order linear equations. For the recursive method, we generalize the least squares method and develop a particle prediction error identification algorithm so that it can be applied to general nonlinear stochastic systems. We use the branching particle filter to do the signal state estimation and implement simulations for both methods.

• Date created

  2003

• Subjects / Keywords

  • Asymptotic method of moments
  • Nonlinear stochastic system
  • Filtering
  • Particle filters
  • Particle prediction error identification method
  • Parameter estimation

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/R39882V1Z

• License

  Copyright 2003 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.