Robust Kalman Filtering and Its Applications.

Abstract

This paper presents a robust Kalman filtering algorithm that is obtained assuming a scale contaminated normal distribution for the noise of the measurement equation. The mixture of normals obtained as a posterior distribution is approximated at each stage by a normal distribution with the same mean and variance. The resulting algorithm is simple, has a straightforward interpretation and seems to provide useful robust estimators in several statistical problems that are briefly reviewed. Originator-supplied keywords include: Robustness, and mixtures of normals.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1984
Accession Number
ADA149044

Entities

People

  • D. Pena
  • I. Guttman

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computations
  • Data Sets
  • Equations
  • Estimators
  • Filters
  • Filtration
  • Kalman Filtering
  • Kalman Filters
  • Mathematical Filters
  • Mathematics
  • Normal Distribution
  • Probability
  • Statistical Estimation
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Inertial Navigation Systems.
  • Statistical inference.