Oblique Projections: Formulas, Algorithms, and Error Bounds.

Abstract

When an orthogonal projection is to be computed using data acquired by distributed sensors, it is often necessary to process each sensor's data locally and then transmit the results to a central facility for final processing. The most efficient way to do this is to compute oblique projections locally. This choice makes the final processing a matter of summing the oblique projections. This paper derives new formulas, and iterative algorithms and associated error bounds, for oblique projections in arbitrary Hilbert spaces.

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Document Details

Document Type
Technical Report
Publication Date
Jun 02, 1987
Accession Number
ADA181914

Entities

People

  • Howard L. Weinert
  • Selahattin Kayalar

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Banach Space
  • Classification
  • Computations
  • Contracts
  • Data Acquisition
  • Data Science
  • Data Sets
  • Decomposition
  • Hilbert Space
  • Military Research
  • Notation
  • Procurement
  • Stationary Processes
  • Statistical Algorithms
  • Stochastic Processes
  • Vector Spaces

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.
  • Nanofabrication and Microfabrication.

Technology Areas

  • Space