Open Access

Simultaneous Principal-Component Extraction with Application to Adaptive Blind Multiuser Detection

  • Deniz Erdogmus1Email author,
  • Yadunandana N. Rao1,
  • Kenneth E. Hild II1 and
  • Jose C. Principe1
EURASIP Journal on Advances in Signal Processing20032002:567865

DOI: 10.1155/S1110865702210033

Received: 31 January 2002

Published: 2 January 2003

Abstract

SIPEX-G is a fast-converging, robust, gradient-based PCA algorithm that has been recently proposed by the authors. Its superior performance in synthetic and real data compared with its benchmark counterparts makes it a viable alternative in applications where subspace methods are employed. Blind multiuser detection is one such area, where subspace methods, recently developed by researchers, have proven effective. In this paper, the SIPEX-G algorithm is presented in detail, convergence proofs are derived, and the performance is demonstrated in standard subspace problems. These sub space problems include direction of arrival estimation for incoming signals impinging on a linear array of sensors, nonstationary random process subspace tracking, and adaptive blind multiuser detection.

Keywords

principal components analysis multiuser detection SIPEX

Authors’ Affiliations

(1)
Computational NeuroEngineering Laboratory, Electrical and Computer Engineering Department, University of Florida

Copyright

© Erdogmus et al. 2002