A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes

Abstract

Nonlinear filtering is the process of estimating and tracking the state of a nonlinearstochastic system from non-Gaussian noisy observation data. In this technical memorandum,we present an overview of techniques for nonlinear filtering for a wide varietyof conditions on the nonlinearities and on the noise. We begin with the developmentof a general Bayesian approach to filtering which is applicable to all linear or nonlinearstochastic systems. We show how Bayesian filtering requires integration over probabilitydensity functions that cannot be accomplished in closed form for the general nonlinear,non-Gaussian multivariate system, so approximations are required.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
AD1125123

Entities

People

  • A. J. Haug

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • C4I
  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Fluid Dynamics
  • Computational Science
  • Coordinate Systems
  • Data Science
  • Distribution Functions
  • Electrical Engineering
  • Filters
  • Filtration
  • Gaussian Distributions
  • Gaussian Processes
  • Information Processing
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Monte Carlo Method
  • Probability
  • Probability Distributions
  • Random Variables
  • Sequential Monte Carlo Methods
  • Signal Processing
  • Statistical Algorithms
  • Target Tracking

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Distributed Systems and Data Platform Development
  • Organizational Process Management (OPM).

Technology Areas

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