An Adaptive Estimator for Time Varying Processes with Maneuvers.

Abstract

An adaptive discrete Kalman filter is developed which is based on an improved covariance matching technique. The filter quickly finds the unknown process noise covariance implied by the observed data. The process noise covariance is assumed constant. all other filter parameters are assumed known, although possibly time varying. This filter, used in conjunction with a multiple model filter, can be used to iteratively estimate noise statistics and detect maneuvers. (Author)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA172837

Entities

People

  • Michael A. Schiefer

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Estimators
  • Filters
  • Information Science
  • Kalman Filters
  • Maneuvers
  • Mathematics
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Fields of Study

  • Engineering

Readers

  • Inertial Navigation Systems.
  • Statistical inference.