Estimator Performance for a Class of Nonlinear Estimation Problems.

Abstract

The state estimation problem for a certain class of nonlinear stochastic systems with white Gaussian plant and observation noise is considered. The optimal (minimum variance) estimators for these systems are recursive and finite dimensional. A particular nonlinear system which contains a polynomial nonlinearity is presented. Both optimal and suboptimal estimators and an estimation lower bound for such a system are derived. The performance of the optimal and suboptimal estimators and the lower bound are compared both analytically and by computer simulation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 23, 1979
Accession Number
ADA067661

Entities

People

  • Chang-huan Liu
  • Steven I Marcus

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Air Force
  • Computational Science
  • Covariance
  • Data Science
  • Differential Equations
  • Electrical Engineering
  • Engineering
  • Information Science
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Monte Carlo Method
  • Nonlinear Systems
  • Optimal Estimators
  • Partial Differential Equations
  • Simulations
  • Statistical Analysis

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.