Subspace Signal Processing in Structured Noise

Abstract

Some common types of noise can be dealt with by applying a linear model to the noise as well as to the signal. The noise obeys a low-rank linear model as structured noise and derive several signal processing methods based on a structured noise model. Whereas orthogonal projection operators play a key role in the solution of classical linear estimation and detection problems, the addition of a structured noise term to the model leads to oblique projection operators in the new solutions. Consider several subspace identification problems in the context of a structured noise model. Also consider parameter estimation with structured noise, where the signal and structured noise subspaces are known or have been identified from observed data. These results are applied to the decoding of complex number codes for detection and correction of impulse errors.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA231396

Entities

People

  • Richard T. Behrens

Organizations

  • University of Colorado Boulder

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Coding
  • Communication Systems
  • Complex Numbers
  • Decoding
  • Detection
  • Digital Signal Processing
  • Equations
  • Estimators
  • Gaussian Distributions
  • Information Theory
  • Linear Algebra
  • Mathematics
  • Notation
  • Numbers
  • Signal Processing
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

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