Gram-Schmidt Implementation of a Linearly Constrained Adaptive Array.
NAVAL RESEARCH LAB WASHINGTON DC
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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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