The Least Squares Lattice Algorithm: An Alternate Derivation with a Discussion of Numerical Conditioning.

Abstract

A new derivation of the least squares lattice algorithm is given in which the filter coefficients are solved for directly by a Gram-Schmidt orthogonalization of the data. This approach shows that the unnormalized and normalized least squares lattice algorithms have the same numerical conditioning as the so-called normal equations associated with least squares problems. Thus, contrary to some of the literature, the normalized lattice is not numerically superior to the unnormalized lattice for ill-conditioned problems. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1984
Accession Number
ADA143439

Entities

People

  • S. Kalson

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Computer Programming
  • Computers
  • Covariance
  • Data Science
  • Decomposition
  • Digital Computers
  • Dynamic Programming
  • Equations
  • Filters
  • Information Science
  • Kalman Filters
  • Numbers
  • Residuals
  • Square Roots
  • Statistics

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  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience