The Bayesian Approach to Recursive State Estimation: Implementation and Application.

Abstract

In Bayesian estimation, the objective is to calculate the complete density function for an unknown quantity conditioned on noisy observations of that quantity. This work considers recursive estimation of a nonlinear discrete-time system state using successive observations. The formal recursion for the density function is easily written, but generally there is no closed form solution. The numerical solution proposed here is obtained by modifying the recursion and using a simple piece-wise constant approximation to the density functions. The critical part of the algorithm then becomes a discrete linear convolution that can be realized using FFT's. Keywords: error analysis; and parameter estimation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1985
Accession Number
ADA160836

Entities

People

  • S. C. Kramer

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Cyber
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Bayesian Networks
  • Computational Complexity
  • Computational Science
  • Computer Programs
  • Data Science
  • Estimators
  • Filtration
  • Geometry
  • Information Science
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Optimal Estimators
  • Statistical Analysis
  • Statistics

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Approximation Theory.
  • Calculus or Mathematical Analysis
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms