Signal Processing in Subspaces.

Abstract

Reduced dimension or subspace signal processing algorithms are studied for several classes of signal processing problems. The approach consists of mapping data into a subspace with a rectangular matrix transformation prior to application of the signal processing algorithm. This approach reduces computational complexity, reduces the variability associated with quantities estimated from data, and generally introduces some asymptotic performance loss. However, in situations with limited data, the impact of reduced variance generally dominates the asymptotic performance loss and a net improvement in performance is obtained. Application of this principle and the corresponding performance and computational complexity tradeoffs are discussed for adaptive beamforming and detection, Volterra filtering, and estimation of higher order statistics.

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Document Details

Document Type
Technical Report
Publication Date
Mar 12, 1997
Accession Number
ADA324997

Entities

People

  • Barry Van Veen

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Communication Systems
  • Computational Complexity
  • Data Science
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Filtration
  • Information Processing
  • Information Science
  • Nonlinear Systems
  • Order Statistics
  • Signal Processing
  • Statistics

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematics or Statistics