WARP convergence in conjugate gradient wiener filters
Document Type
Conference Proceeding
Publication Date
12-1-2004
Abstract
In this work, we present interesting case studies that lead to new and deeper results on fast convergence of reduced-rank conjugate gradient (RRCG) Wiener filters (WF), for applications in communications. and sensor array signal processing. We discover that for signal modes with a specially structured Gram matrix, which induces L groups of distinct eigenvalues in the data covariance matrix, a fast and predictable convergence, in at most L steps, can be achieved when the RRCG WF is used to detect, and/or to focus on, the desired signal mode. For such applications, given knowledge of the repeated eigenstructure of the Gram matrix of signal modes or of the measurement covariance matrix, a RRCG Wiener filter, of at most rank L, delivers the same performance as the full-rank Wiener filter. Typically L is much less than the rank of the Gram matrix. ©2004 IEEE.
Identifier
28244467901 (Scopus)
ISBN
[0780385454]
Publication Title
2004 Sensor Array and Multichannel Signal Processing Workshop
First Page
109
Last Page
113
Recommended Citation
Ge, Hongya; Lundberg, Magnus; and Scharf, Louis L., "WARP convergence in conjugate gradient wiener filters" (2004). Faculty Publications. 20120.
https://digitalcommons.njit.edu/fac_pubs/20120
