NONLINEAR FILTERING BY APPROXIMATION OF THE A POSTERIORI DENSITY,
Abstract
The problem of estimating from noisy measurement data the state of a dynamical system described by nonlinear difference equations is considered. The measurement data have a nonlinear relation with the state and are assumed to be available at discrete instants of time. A Bayesian approach to the problem is suggested in which the density function for the state conditioned upon the available measurement data is computed recursively. The evolution of the a posteriori density function cannot be described in a closed form for most systems; the class of linear systems with additive, white gaussian noise provides the major exception. Thus, the problem of nonlinear filtering can be viewed as essentially, a problem of approximating this density function. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1967
- Accession Number
- AD0709228
Entities
People
- A. R. Stubberud
- H. W. Sorenson
Organizations
- University of California, Los Angeles