The Bayesian Approach to Recursive State Estimation: Implementation and Application.

Abstract

In Bayesian estimation, the objective is to calculate the complete density function for an unknown quantity conditioned on noisy observations of that quantity. This work considers recursive estimation of a nonlinear discrete-time system state using successive observations. The formal recursion for the density function is easily written, but generally there is no closed form solution. The numerical solution proposed here is obtained by modifying the recursion and using a simple piece-wise constant approximation to the density functions. The critical part of the algorithm then becomes a discrete linear convolution that can be realized using FFT's. Keywords: error analysis; and parameter estimation.

Cite this paper

@article{kramer1985bayesian,
  title = {{The Bayesian Approach to Recursive State Estimation: Implementation and Application.}},
  author = {Kramer, Stuart},
  journal = {Defense Technical Information Center (DTIC)},
  year = {1985}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers