Optimal State-Vector Estimation for Non-Gaussian Initial State-Vector,

Abstract

The optimal, in the mean-square error sense, estimate of state-vector of a linear system excited by zero-mean white Gaussian noise with non-Gaussian initial state-vector is obtained. Both the optimal estimate and the corresponding error covariance matrix are given. It is shown that the optimal estimator consists of two parts: a linear estimator which is obtained from Kalman filter and a nonlinear estimator. In addition the a-posteriori probability, P(x sub k/lambda sub k) is also given.

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1971
Accession Number
AD0722081

Entities

People

  • D. G. Lainiotis
  • R. Krishnaiah
  • S. K. Park

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Covariance
  • Estimators
  • Filters
  • Gaussian Noise
  • Kalman Filters
  • Linear Systems
  • Mathematics
  • Noise
  • Optimal Estimators
  • Probability
  • Statistical Algorithms
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.