Robustifying the Kalman Filter.

Abstract

Kalman filters are tracking and prediction algorithms based on Gaussian measurement errors and structural models. The Kalman filter performance may degrade if the measurement errors come from a thicker-tailed-than Gaussian distribution. In this report non-linear procedures are described which are based on Kalman-type models, but work with student-t measurement errors. Keywords: Kalman filter; Student-t measurement errors; Iterative reweighting procedure; Nonlinear filter; Biweight; Robust estimation.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1987
Accession Number
ADA189240

Entities

People

  • Donald P. Gaver
  • P. A. Jacobs

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Estimators
  • Filters
  • Gaussian Distributions
  • Industrial Engineering
  • Iterations
  • Kalman Filtering
  • Kalman Filters
  • Measurement
  • Military Research
  • Normal Distribution
  • Operations Research
  • Random Variables
  • Schools
  • Simulations
  • Statistics
  • Students

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.