Bounds on the Accuracy in Causal Filtering for Nonlinear Observations with Some Implications on Asymptotic Separation in Stochastic Control,

Abstract

A bound is derived on the accuracy in causally estimating a Gaussian process from nonlinear observations. Both additive Gaussian noise and Poisson observations are included. The bound is used to study the control of a stochastic linear dynamical system with nonlinear observations and an average quadratic cost. An asymptotic separation theorem is established showing that a linear feedback control law, involving a state estimate, is asymptotically optimum as the accuracy of the state estimate approaches the bound. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 30, 1971
Accession Number
AD0736799

Entities

People

  • Donald L. Snyder
  • Ian B. Rhodes

Organizations

  • University of Washington

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Additives (Chemicals)
  • Feedback
  • Filtration
  • Gaussian Noise
  • Gaussian Processes
  • Noise
  • Observation
  • Stochastic Control

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.