A Continuum of Models for Stochastic Estimation

Abstract

In this paper, we investigate a recursive multiple model tracking approach similar to the Generalized Pseudo-Bayesian I (GPBI) I approach. However, here we consider a continuum of models rather than the discrete set that is usually implemented in the GPBI method. By doing so better models are available to improve tracker performance and solve the bias problem inherent in most multiple model approaches.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 20, 2000
Accession Number
ADA392874

Entities

People

  • Jeffery Layne
  • Scott Weaver

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Avionics
  • Computations
  • Covariance
  • Data Fusion
  • Equations
  • Filters
  • Kalman Filters
  • Measurement
  • Night Vision
  • Probability
  • Probability Density Functions
  • Uncertainty
  • United States

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference