Risk-Sensitive Filtering and Parameter Estimation

Abstract

This paper investigates the use of risk-sensitive filtering for state and parameter estimation in systems with model uncertainties. Modelling uncertainties arise from imperfectly known input process and noise characteristics as well as system model errors such as uncertain or time varying parameters of the system description. No new convergence results are given in this paper but simulation examples demonstrate that, in some situations, risk-sensitive filtering and estimation techniques allow for system uncertainties better than optimal techniques such as Kalman filtering.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1999
Accession Number
ADA361768

Entities

People

  • Jason Ford

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Australia
  • Control Systems
  • Convergence
  • Dynamics
  • Engineering
  • Estimators
  • Guidance
  • Kalman Filters
  • Linear Systems
  • Probability
  • Random Variables
  • Sequences
  • Standards
  • Universities
  • Weapons

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.