A SENSITIVITY ALGORITHM FOR THE KALMAN FILTER AND PREDICTOR,

Abstract

In this investigation sensitivity matrix algorithms for the continuous-time Kalman filter and predictor are derived. Nonlinear matrix differential equations of the Riccati type are derived. The solutions of these equations represent the increase in the error covariance matrix of a suboptimal estimator over the optimal estimator. These solutions permit the sensitivity analysis to be performed a priori on linear estimation problems. The objective of this investigation was to provide the designer a technique to simplify the sensitivity analysis of the Kalman filter and predictor. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1970
Accession Number
AD0701427

Entities

People

  • Demetrios G. Lainiotis
  • Malcolm R. Railey

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Covariance
  • Differential Equations
  • Equations
  • Estimators
  • Filters
  • Kalman Filters
  • Mathematical Analysis
  • Mathematical Filters
  • Optimal Estimators
  • Sensitivity
  • Statistical Algorithms
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.