A Comparison of Noise Generation Techniques and the Effects on Inverse Problem Calculations

Abstract

To test the performance of an algorithm for an inverse problem for cases similar to those under which experimental data is used, random noise can be added to given simulated data. The most common way to do this is to use white noise generated by a uniformly or normally distributed random sequence. Another possibility is to use noise given by the so-called Rice Representation of random noise. We compare results for these two kinds of noise.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA445713

Entities

People

  • H. Thomas Banks
  • Irene Groselj

Organizations

  • North Carolina State University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Computations
  • Demographic Cohorts
  • Experimental Data
  • Frequency
  • Information Operations
  • Inverse Problems
  • Noise
  • North Carolina
  • White Noise

Readers

  • Acoustics.
  • Computational Modeling and Simulation
  • Statistical inference.