A Distributed and Iterative Method for Square Root Filtering in Space-Time Estimation

Abstract

We describe a distribute, and iterative approach to perform the unitary transformations in the square root information filter imple nentation of the Kalman filter, providing an alternative to the common QR factorization-based approaches. The new approach is useful in approximate computation of filtered estimates for temporally-evolving random fields defined by local interactions and observations. Using several examples motivated by computer vision applications, we demonstrate that near-optimal estimates can be computed for problems of practical importance using only a small number of iterations, which can be performed in a finely parallel manner over the spatial domain of the random field.

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Document Details

Document Type
Technical Report
Publication Date
Jan 19, 1994
Accession Number
ADA459794

Entities

People

  • Alan S. Willsky
  • Toshio M. Chin
  • William C. Karl

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Band Structures
  • Computations
  • Computer Vision
  • Difference Equations
  • Differential Equations
  • Equations
  • Filtration
  • Image Processing
  • Kalman Filtering
  • Kalman Filters
  • Mathematical Filters
  • Partial Differential Equations
  • Square Roots
  • Two Dimensional
  • White Noise

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Fluid Dynamics (CFD)
  • Linear Algebra

Technology Areas

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