Rotation Method for Reconstructing Process and Field From Imperfect Data

Abstract

Reconstruction of processes and fields from noisy data is to solve a set of linear algebraic equations. Three factors affect the accuracy of reconstruction: (a) a large condition number of the coefficient matrix, (b) high noise-to-signal ratio in the source term, and (c) no a priori knowledge of noise statistics. To improve reconstruction accuracy, the set of linear algebraic equations is transformed into a new set with minimum condition number and noise-to-signal ratio using the rotation matrix. The procedure does not require any knowledge of low-order statistics of noises. Several examples including highly distorted Lorenz attractor illustrate the benefit of using this procedure.

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Document Details

Document Type
Technical Report
Publication Date
Aug 28, 2003
Accession Number
ADA479253

Entities

People

  • Leonid M. Ivanov
  • Peter Cheng Chu
  • Tatyana M. Margolina

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Black Sea
  • Coefficients
  • Differential Equations
  • Equations
  • Fourier Series
  • Information Operations
  • Iterations
  • Lepidoptera
  • Linear Algebraic Equations
  • Noise
  • Order Statistics
  • Rotation
  • Statistics
  • Surface Temperature
  • Two Dimensional
  • White Noise

Readers

  • Control Systems Engineering.
  • Regression Analysis.
  • Wave Propagation and Nonlinear Chaotic Dynamics.