Convergence analysis of linearly constrained SMI and LSMI adaptive algorithms

Author

Abramovich, Yuri I.

Author_Institution

CCSIP, Mawson Lakes, SA, Australia

fYear

2000

fDate

2000

Firstpage

255

Lastpage

259

Abstract

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

Keywords

adaptive signal processing; airborne radar; convergence of numerical methods; matrix inversion; probability; radar signal processing; signal sampling; unsupervised learning; airborne radar applications; auxiliary linear constraints; convergence analysis; finite sample support; limited secondary data; linearly constrained LSMI adaptive algorithms; linearly constrained SMI adaptive algorithm; loaded sample matrix inversion; loss factor; matched steering vector; mismatched steering vector; over-the-horizon radar applications; probability densities; supervised training conditions; unsupervised training conditions; Adaptive algorithm; Algorithm design and analysis; Australia; Convergence; Information processing; Interference constraints; Maximum likelihood estimation; Signal processing; Stochastic processes; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000

Conference_Location

Lake Louise, Alta.

Print_ISBN

0-7803-5800-7

Type

conf

DOI

10.1109/ASSPCC.2000.882481

Filename

882481