Convergence analysis of linearly constrained SMI and LSMI adaptive algorithms

The probability densities for the loss factor caused by finite sample support in sample matrix inversion (SMI) and loaded sample matrix inversion (LSMI) adaptive algorithms with auxiliary linear constraints are introduced. Supervised and unsupervised training conditions along with matched and mismat...

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Bibliographic Details
Published inProceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373) pp. 255 - 259
Main Author Abramovich, Yu.I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Adaptive algorithm
Algorithm design and analysis
Australia
Convergence
Information processing
Interference constraints
Maximum likelihood estimation
Signal processing
Stochastic processes
Vectors
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Summary:The probability densities for the loss factor caused by finite sample support in sample matrix inversion (SMI) and loaded sample matrix inversion (LSMI) adaptive algorithms with auxiliary linear constraints are introduced. Supervised and unsupervised training conditions along with matched and mismatched steering vector conditions are considered.
ISBN:0780358007
9780780358003
DOI:10.1109/ASSPCC.2000.882481