SEQUENTIAL ESTIMATION FOR DISCRETE-TIME NONLINEAR SYSTEMS,

Abstract

The problem of state and parameter estimation for noisy discrete-time nonlinear dynamic systems is examined from the viewpoint of marginal maximum likelihood estimation. Approximate algorithms for sequential prediction, filtering, and smoothing are developed. The former two are in agreement with previous results; the latter is new. A technique for iterative-sequential filtering and smoothing using Newton's method is indicated. A numerical example is included to illustrate the results. (Author)

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1969
Accession Number
AD0688244

Entities

People

  • J. S. Meditch

Organizations

  • Boeing

Tags

DTIC Thesaurus Topics

  • Agreements
  • Algorithms
  • Filtration
  • Mathematics
  • Maximum Likelihood Estimation
  • Nonlinear Systems

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.