Gram-Schmidt Implementation of a Linearly Constrained Adaptive Array.

Abstract

A Gram-Schmidt (GS) implementation of the linearly constrained adaptive algorithm proposed by Frost is developed. This implementation is shown to be equivalent to the technique developed by Jim, Griffiths, and Buckley whereby the constrained problem is reduced to an unconstrained problem. In addition, analytical, results are presented for the convergence rate when the Sampled Matrix Inversion (SMI) algorithm is employed. It had been previously shown that the steady state solution for the optimal weights is identical for both constrained and reduced unconstrained problems. In this report, it is shown that if the SMI or GS algorithms are employed, then the transient weighting vector solution for the constrained problem is identical to equivalent transient weighting vector solution for the reduced unconstrained implementation.

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Document Details

Document Type
Technical Report
Publication Date
Feb 26, 1988
Accession Number
ADA191651

Entities

People

  • Karl R. Gerlach

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Antenna Arrays
  • Antennas
  • Arrays
  • Cancellation
  • Classification
  • Convergence
  • Covariance
  • Data Sets
  • Decomposition
  • Equations
  • Military Research
  • Probability
  • Random Variables
  • Security
  • Steady State

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Phased Array Antenna Design.