Optimal Estimation with Two Process Models and No Measurements

Abstract

An observer is presented for combining the predicted states of 2 independent process models when no measurements are present. The observer follows a derivation similar to that of the discrete time Kalman filter. A simulation example is provided in which a process model based on the dynamics of a ballistic projectile is blended with an inertial navigation system. The results show that under the certain conditions, the algorithm provides estimates of the projectile states with less error than either individual process model.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2015
Accession Number
ADA623358

Entities

People

  • James M. Maley

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Accelerometers
  • Algorithms
  • Ballistic Trajectories
  • Covariance
  • Data Science
  • Equations
  • Estimators
  • Filters
  • Inertial Measurement Units
  • Kalman Filters
  • Measurement
  • Microelectromechanical Systems
  • Military Research
  • Projectiles
  • Simulations
  • Statistical Algorithms
  • Trajectories

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • ballistics.