OPTIMAL FILTERING FOR GAUSS-MARKOV NOISE.

Abstract

The optimal continuous-filtering problem for the case of linear dynamics, linear measurements, and gaussian white disturbance and measurement noise has been solved by Kalman and Bucy. In this study, their results are generalized for the case where measurement noise is a Gauss-Markov process, but without augmenting state, as has already been done. Proof is presented that the optimal continuous-filtering problem can be solved by simply replacing the observation vector with a derived observation vector and an initial condition. When the derived observation vector is used, colored noise is eliminated and only the standard Kalman filter problem, easily solvable, remains. (Author)

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1967
Accession Number
AD0665883

Entities

People

  • A. R. Stubberud

Organizations

  • The Aerospace Corporation

Tags

DTIC Thesaurus Topics

  • Dynamics
  • Filters
  • Filtration
  • Kalman Filters
  • Markov Processes
  • Mathematics
  • Measurement
  • Observation
  • Standards

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.